This paper analyses features selection method used in medical image processing. How image is selected by using diverse sort of method similarly: screening, scanning and selecting. We discussed on feature selection procedure which is extensively used for data mining and knowledge discovery and it carryout elimination of redundant features, concomitantly retaining the fundamental bigoted information, feature selection implies less data transmission and efficient data mining. It accentuates the need for further research in the field of pattern recognition that can effectively determine the situation with captured portion of human body.
AN EFFECTIVE AND EFFICIENT FEATURE SELECTION METHOD FOR LUNG CANCER DETECTIONijcsit
This document summarizes a research paper on an effective and efficient feature selection method for lung cancer detection. It discusses how feature selection can reduce the number of features in medical image analysis to extract the most important features for accurate image recognition and classification. The proposed method involves extracting the lung region from CT scans, segmenting the lung tissue, analyzing segments to extract diagnostic features, and applying classification rules to determine if cancer is present or not. Feature selection is shown to improve the performance of automated computer-aided diagnosis systems for early detection of lung cancer.
Feature Selection Mammogram based on Breast Cancer Mining IJECEIAES
The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.
Feature selection/extraction methods aimed to reduce the Microarray data. Basically in this comparative analysis, we have taken into account different feature selection and extraction strategies used up till now in the field of Biomedical. In the field of pattern recognition and biomedical imaging, dimensionality reduction is the central area of the research. Some mostly used features selection/extraction methods aim to analyze the most efficient data and achieve the stable performance of the algorithms, as well as improve the accuracy and performance of the classifier. This analysis also highlights widely used dimensionality reduction techniques used up till now in the field of biomedical imaging for the purpose to explore their potency, and weak points.
BFO – AIS: A FRAME WORK FOR MEDICAL IMAGE CLASSIFICATION USING SOFT COMPUTING...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in
database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),
Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.
CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more
reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio
therapy. Medical information systems goals are to deliver information to right persons at the right time and
place to improve care process quality and efficiency. This paper proposes an Artificial Immune System
(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)
with Local Search (LS) for medical image classification.
A novel medical image segmentation and classification using combined feature ...eSAT Journals
Abstract Diagnosis is the first step before giving a medicine to the patient. In the recent past such diagnosis is performed using medical images where segmentation is the prime part in the medical image retrieval which improves the feature set that is collected from the segmented image. In this paper, it is proposed to segment the medical image a semi decision algorithm that can segment only the tumor part from the CT image. Further texture based techniques are used to extract the feature vector from the segmented region of interest. Medical images under test are classified using decision tree classifier. Results show better performance in terms of accuracy when compared to the conventional methods. Key Words: Medical Images, Decision Tree Classifier, Segmentation, Semi-decision algorithm
IRJET - Lung Disease Prediction using Image Processing and CNN AlgorithmIRJET Journal
This document summarizes a research paper that proposes a method for predicting lung disease using image processing and convolutional neural networks (CNNs). The method involves preprocessing chest x-ray images through steps like lung field segmentation, feature extraction, and then classifying the images as normal or abnormal using neural networks and support vector machines (SVMs). The researchers tested their approach on two datasets and were able to classify digital chest x-ray images into normal and abnormal categories with high accuracy. The goal of the research is to develop an automated system for early detection of lung cancer using chest x-rays, as early detection is key to better treatment outcomes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparison of Feature selection methods for diagnosis of cervical cancer usin...IJERA Editor
Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the
pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist
to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is
obtained by Pap smear test. Image segmentation performed by multi-thresholding method and texture and shape
features are extracted related to cervical cancer. Feature selection is achieved using Mutual Information(MI),
Sequential Forward Search (SFS), Sequential Floating Forward Search (SFFS) and Random Subset Feature
Selection(RSFS) methods.
AN EFFECTIVE AND EFFICIENT FEATURE SELECTION METHOD FOR LUNG CANCER DETECTIONijcsit
This document summarizes a research paper on an effective and efficient feature selection method for lung cancer detection. It discusses how feature selection can reduce the number of features in medical image analysis to extract the most important features for accurate image recognition and classification. The proposed method involves extracting the lung region from CT scans, segmenting the lung tissue, analyzing segments to extract diagnostic features, and applying classification rules to determine if cancer is present or not. Feature selection is shown to improve the performance of automated computer-aided diagnosis systems for early detection of lung cancer.
Feature Selection Mammogram based on Breast Cancer Mining IJECEIAES
The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.
Feature selection/extraction methods aimed to reduce the Microarray data. Basically in this comparative analysis, we have taken into account different feature selection and extraction strategies used up till now in the field of Biomedical. In the field of pattern recognition and biomedical imaging, dimensionality reduction is the central area of the research. Some mostly used features selection/extraction methods aim to analyze the most efficient data and achieve the stable performance of the algorithms, as well as improve the accuracy and performance of the classifier. This analysis also highlights widely used dimensionality reduction techniques used up till now in the field of biomedical imaging for the purpose to explore their potency, and weak points.
BFO – AIS: A FRAME WORK FOR MEDICAL IMAGE CLASSIFICATION USING SOFT COMPUTING...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in
database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),
Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.
CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more
reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio
therapy. Medical information systems goals are to deliver information to right persons at the right time and
place to improve care process quality and efficiency. This paper proposes an Artificial Immune System
(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)
with Local Search (LS) for medical image classification.
A novel medical image segmentation and classification using combined feature ...eSAT Journals
Abstract Diagnosis is the first step before giving a medicine to the patient. In the recent past such diagnosis is performed using medical images where segmentation is the prime part in the medical image retrieval which improves the feature set that is collected from the segmented image. In this paper, it is proposed to segment the medical image a semi decision algorithm that can segment only the tumor part from the CT image. Further texture based techniques are used to extract the feature vector from the segmented region of interest. Medical images under test are classified using decision tree classifier. Results show better performance in terms of accuracy when compared to the conventional methods. Key Words: Medical Images, Decision Tree Classifier, Segmentation, Semi-decision algorithm
IRJET - Lung Disease Prediction using Image Processing and CNN AlgorithmIRJET Journal
This document summarizes a research paper that proposes a method for predicting lung disease using image processing and convolutional neural networks (CNNs). The method involves preprocessing chest x-ray images through steps like lung field segmentation, feature extraction, and then classifying the images as normal or abnormal using neural networks and support vector machines (SVMs). The researchers tested their approach on two datasets and were able to classify digital chest x-ray images into normal and abnormal categories with high accuracy. The goal of the research is to develop an automated system for early detection of lung cancer using chest x-rays, as early detection is key to better treatment outcomes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparison of Feature selection methods for diagnosis of cervical cancer usin...IJERA Editor
Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the
pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist
to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is
obtained by Pap smear test. Image segmentation performed by multi-thresholding method and texture and shape
features are extracted related to cervical cancer. Feature selection is achieved using Mutual Information(MI),
Sequential Forward Search (SFS), Sequential Floating Forward Search (SFFS) and Random Subset Feature
Selection(RSFS) methods.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A new swarm intelligence information technique for improving information bala...IJECEIAES
Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage.
IRJET-Implementation of CAD system for Cancer Detection using SVM based Class...IRJET Journal
This document presents a proposed CAD system for cancer detection using SVM classification. The system aims to automatically detect, segment, and classify breast masses in mammograms. It first extracts the region of interest from mammograms and performs segmentation using fuzzy C-means clustering. It then extracts texture and geometric features from segmented masses. Feature selection is used to select the most important features, which are then classified as benign or malignant using an SVM classifier. The proposed system seeks to develop a fully automated CAD system for breast cancer detection and classification without manual intervention.
This document summarizes the design and results of a subjective quality assessment study of a new medical image database.
The database contains 100 test medical images, including 20 reference images and 5 types of distortions applied to each reference image. Fifteen doctors evaluated the images using a double stimulus impairment scale protocol and provided mean opinion scores (MOS) for each distorted image. The collected MOS data can be used to evaluate the performance of visual quality metrics and help design new metrics, as well as test numerical observer models for medical image quality assessment.
Unsupervised Feature Selection Based on the Distribution of Features Attribut...Waqas Tariq
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
IRJET-Android Based Plant Disease Identification System using Feature Extract...IRJET Journal
This document summarizes a research paper that proposes an Android-based plant disease identification system using image processing techniques. The system uses feature extraction and pattern matching to analyze images of infected plant leaves and identify the disease. It segments images using k-means clustering and extracts features like color, morphology, texture and lesions. These features are matched against a trained database of infected images using SURF pattern matching to diagnose the disease. The system is intended to help farmers and experts identify diseases early at a lower cost than other methods. It aims to achieve accurate identification of diseases for grapes, apples and pomegranates through this automated mobile-based approach.
IRJET- Machine Learning Classification Algorithms for Predictive Analysis in ...IRJET Journal
This document discusses machine learning classification algorithms and their applications for predictive analysis in healthcare. It provides an overview of data mining techniques like association, classification, clustering, prediction, and sequential patterns. Specific classification algorithms discussed include Naive Bayes, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Neural Networks, and Bayesian Methods. The document examines examples of these algorithms being used for disease diagnosis, prognosis, and healthcare management. It analyzes their predictive performance on datasets for conditions like breast cancer, heart disease, and ICU readmissions. Overall, the document reviews how machine learning techniques can enhance predictive accuracy for various healthcare problems.
Palm print recognition based on harmony search algorithm IJECEIAES
Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.
Automated face recognition offers an effective method for identifying individuals. Face images have been used in a number of different applications, including driver’s licenses, passports and identification cards. To provide some form of standardization for photographs in these applications, ISO / IEC JTC 1 SC 37 have developed standardized data interchange formats to promote interoperability. There are many different publically available face databases available to the research community that are used to advance the field of face recognition algorithms, amongst other uses. In this paper, we examine how an existing database that has been used extensively in research (FERET) compares with two operational data sets with respect to some of the metrics outlined in the standard ISO / IEC 19794-5. The goals of this research are to provide the community with a comparison of a baseline data set and to compare this baseline to a photographic data set that has been scanned in from mug-shot photographs, as well as a data set of digitally captured photographs. It is hoped that this information will provide Face Recognition System (FRS) developers some guidance on the characteristics of operationally collected data sets versus a controlled-collection database.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Review of Image Watermarking Technique for MediIJARIIT
In this article, we focus on the complementary role of watermarking with respect to medical information security (integrity, authenticity …) and management. We review sample cases where watermarking has been deployed. We conclude that watermarking has found a niche role in healthcare systems, as an instrument for protection of medical information, for secure sharing and handling of medical images. The concern of medical experts on the preservation of documents diagnostic integrity remains paramount. Medical image watermarking is an appropriate method used for enhancing security and authentication of medical data, which is crucial and used for further diagnosis and reference. This paper discusses the available medical image watermarking methods for protecting and authenticating medical data. The paper focuses on algorithms for application of watermarking technique on Region of Non Interest (RONI) of the medical image preserving Region of Interest (ROI).
A NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO E...ijcsa
This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
IRJET - Detection and Classification of Brain TumorIRJET Journal
This document presents a novel method for classifying brain MRI images as normal or abnormal using tumor detection. The method first uses wavelet transforms to extract features from images. It then applies principal component analysis to reduce the feature dimensions. The reduced features are input to a kernel support vector machine for classification. A k-fold cross validation strategy is used to enhance the generalization of the support vector machine model. The proposed system takes MRI brain images as input, detects any tumors by highlighting the affected area, and specifies tumor characteristics like dimensions and type (benign or malignant).
MEDICAL IMAGES AUTHENTICATION THROUGH WATERMARKING PRESERVING ROIhiij
Telemedicine is a well-known application where enormous amount of medical data need to be securely
transferred over the public network and manipulate effectively. Medical image watermarking is an
appropriate method used for enhancing security and authentication of medical data, which is crucial and
used for further diagnosis and reference. This project focuses on the study of medical image
watermarking methods for protecting and authenticating medical data. Additionally, it covers algorithm
for application of water marking technique on Region of Non Interest (RONI) of the medical image
preserving Region of Interest (ROI). The medical images can be transferred securely by embedding
watermarks in RONI allowing verification of the legitimate changes at the receiving end without affecting
ROI. Segmentation plays an important role in medical image processing for separating the ROI from
medical image. The proposed system separate the ROI from medical image by GUI based approach,
which works for all types of medical images. The experimental results show the satisfactory performance
of the system to authenticate the medical images preserving ROI.
This document presents an intelligent visualization framework for multi-dimensional data sets. The framework includes pre-processing, feature selection, classification, rule refinement, and visualization phases. In the feature selection phase, principal component analysis and rough sets are used to select important features. Classification is done using rough set rules generation. The rules are then refined using entropy and genetic algorithms. Finally, the refined rules and reducts are visualized using nodes, edges, charts and grids to help experts understand the data. Experimental results on breast cancer and prostate cancer data sets demonstrate the performance of the approach.
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...IRJET Journal
This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.
New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filt...CSCJournals
Ultrasound (US) imaging is an important medical diagnostic method, as it allows the examination of several internal body organs. However, its usefulness is diminished by signal dependent noise known as speckle noise. Speckle noise degrades target detectability in ultrasound images and reduces contrast and resolution, affecting the ability to identify normal and pathological tissue. For accurate diagnosis, it is important to remove this noise from ultrasound images. In this study, a new filtering technique is proposed for removing speckle noise from medical ultrasound images. It is based on Gabor filtering. Specifically, a preprocessing step is added before applying the Gabor filter. The proposed technique is applied to various ultrasound images, and certain measurement indexes are calculated, such as signal to noise ratio, peak signal to noise ratio, structure similarity index, and root mean square error, which are used for comparison. In particular, five widely used image enhancement techniques were applied to three types of ultrasound images (kidney, abdomen and ortho). The main objective of image enhancement is to obtain a highly detailed image, and in that respect, the proposed technique proved superior to other widely used filters.
Decision Tree Classifiers to determine the patient’s Post-operative Recovery ...Waqas Tariq
Machine Learning aims to generate classifying expressions simple enough to be understood easily by the human. There are many machine learning approaches available for classification. Among which decision tree learning is one of the most popular classification algorithms. In this paper we propose a systematic approach based on decision tree which is used to automatically determine the patient’s post–operative recovery status. Decision Tree structures are constructed, using data mining methods and then are used to classify discharge decisions.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
This document presents a hybrid algorithm using biogeography-based optimization (BBO) and ant colony optimization (ACO) for land cover feature extraction from remote sensing images. The algorithm first analyzes a training image to identify features that BBO and ACO classify efficiently. It then applies BBO to clusters containing these features and ACO to remaining clusters. An evaluation shows the hybrid algorithm achieves a higher kappa coefficient of 0.97 compared to 0.67 for BBO alone, indicating better classification accuracy. The authors conclude the algorithm effectively handles uncertainties in remote sensing images and future work could improve efficiency further.
Image classification, remote sensing, P K MANIP.K. Mani
Image classification involves using spectral bands of images to separate landscape features into categories. Pixels with similar spectral signatures are clustered and classified using techniques like maximum likelihood classification. This results in a classified image map where each pixel is assigned a land cover class. However, classified maps have errors, so accuracy assessment is important to estimate the map's accuracy. Supervised classification involves using training areas of known land cover to develop spectral signatures for classification, while unsupervised classification clusters pixels without prior class definitions.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A new swarm intelligence information technique for improving information bala...IJECEIAES
Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage.
IRJET-Implementation of CAD system for Cancer Detection using SVM based Class...IRJET Journal
This document presents a proposed CAD system for cancer detection using SVM classification. The system aims to automatically detect, segment, and classify breast masses in mammograms. It first extracts the region of interest from mammograms and performs segmentation using fuzzy C-means clustering. It then extracts texture and geometric features from segmented masses. Feature selection is used to select the most important features, which are then classified as benign or malignant using an SVM classifier. The proposed system seeks to develop a fully automated CAD system for breast cancer detection and classification without manual intervention.
This document summarizes the design and results of a subjective quality assessment study of a new medical image database.
The database contains 100 test medical images, including 20 reference images and 5 types of distortions applied to each reference image. Fifteen doctors evaluated the images using a double stimulus impairment scale protocol and provided mean opinion scores (MOS) for each distorted image. The collected MOS data can be used to evaluate the performance of visual quality metrics and help design new metrics, as well as test numerical observer models for medical image quality assessment.
Unsupervised Feature Selection Based on the Distribution of Features Attribut...Waqas Tariq
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
IRJET-Android Based Plant Disease Identification System using Feature Extract...IRJET Journal
This document summarizes a research paper that proposes an Android-based plant disease identification system using image processing techniques. The system uses feature extraction and pattern matching to analyze images of infected plant leaves and identify the disease. It segments images using k-means clustering and extracts features like color, morphology, texture and lesions. These features are matched against a trained database of infected images using SURF pattern matching to diagnose the disease. The system is intended to help farmers and experts identify diseases early at a lower cost than other methods. It aims to achieve accurate identification of diseases for grapes, apples and pomegranates through this automated mobile-based approach.
IRJET- Machine Learning Classification Algorithms for Predictive Analysis in ...IRJET Journal
This document discusses machine learning classification algorithms and their applications for predictive analysis in healthcare. It provides an overview of data mining techniques like association, classification, clustering, prediction, and sequential patterns. Specific classification algorithms discussed include Naive Bayes, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Neural Networks, and Bayesian Methods. The document examines examples of these algorithms being used for disease diagnosis, prognosis, and healthcare management. It analyzes their predictive performance on datasets for conditions like breast cancer, heart disease, and ICU readmissions. Overall, the document reviews how machine learning techniques can enhance predictive accuracy for various healthcare problems.
Palm print recognition based on harmony search algorithm IJECEIAES
Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.
Automated face recognition offers an effective method for identifying individuals. Face images have been used in a number of different applications, including driver’s licenses, passports and identification cards. To provide some form of standardization for photographs in these applications, ISO / IEC JTC 1 SC 37 have developed standardized data interchange formats to promote interoperability. There are many different publically available face databases available to the research community that are used to advance the field of face recognition algorithms, amongst other uses. In this paper, we examine how an existing database that has been used extensively in research (FERET) compares with two operational data sets with respect to some of the metrics outlined in the standard ISO / IEC 19794-5. The goals of this research are to provide the community with a comparison of a baseline data set and to compare this baseline to a photographic data set that has been scanned in from mug-shot photographs, as well as a data set of digitally captured photographs. It is hoped that this information will provide Face Recognition System (FRS) developers some guidance on the characteristics of operationally collected data sets versus a controlled-collection database.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Review of Image Watermarking Technique for MediIJARIIT
In this article, we focus on the complementary role of watermarking with respect to medical information security (integrity, authenticity …) and management. We review sample cases where watermarking has been deployed. We conclude that watermarking has found a niche role in healthcare systems, as an instrument for protection of medical information, for secure sharing and handling of medical images. The concern of medical experts on the preservation of documents diagnostic integrity remains paramount. Medical image watermarking is an appropriate method used for enhancing security and authentication of medical data, which is crucial and used for further diagnosis and reference. This paper discusses the available medical image watermarking methods for protecting and authenticating medical data. The paper focuses on algorithms for application of watermarking technique on Region of Non Interest (RONI) of the medical image preserving Region of Interest (ROI).
A NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO E...ijcsa
This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
IRJET - Detection and Classification of Brain TumorIRJET Journal
This document presents a novel method for classifying brain MRI images as normal or abnormal using tumor detection. The method first uses wavelet transforms to extract features from images. It then applies principal component analysis to reduce the feature dimensions. The reduced features are input to a kernel support vector machine for classification. A k-fold cross validation strategy is used to enhance the generalization of the support vector machine model. The proposed system takes MRI brain images as input, detects any tumors by highlighting the affected area, and specifies tumor characteristics like dimensions and type (benign or malignant).
MEDICAL IMAGES AUTHENTICATION THROUGH WATERMARKING PRESERVING ROIhiij
Telemedicine is a well-known application where enormous amount of medical data need to be securely
transferred over the public network and manipulate effectively. Medical image watermarking is an
appropriate method used for enhancing security and authentication of medical data, which is crucial and
used for further diagnosis and reference. This project focuses on the study of medical image
watermarking methods for protecting and authenticating medical data. Additionally, it covers algorithm
for application of water marking technique on Region of Non Interest (RONI) of the medical image
preserving Region of Interest (ROI). The medical images can be transferred securely by embedding
watermarks in RONI allowing verification of the legitimate changes at the receiving end without affecting
ROI. Segmentation plays an important role in medical image processing for separating the ROI from
medical image. The proposed system separate the ROI from medical image by GUI based approach,
which works for all types of medical images. The experimental results show the satisfactory performance
of the system to authenticate the medical images preserving ROI.
This document presents an intelligent visualization framework for multi-dimensional data sets. The framework includes pre-processing, feature selection, classification, rule refinement, and visualization phases. In the feature selection phase, principal component analysis and rough sets are used to select important features. Classification is done using rough set rules generation. The rules are then refined using entropy and genetic algorithms. Finally, the refined rules and reducts are visualized using nodes, edges, charts and grids to help experts understand the data. Experimental results on breast cancer and prostate cancer data sets demonstrate the performance of the approach.
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...IRJET Journal
This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.
New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filt...CSCJournals
Ultrasound (US) imaging is an important medical diagnostic method, as it allows the examination of several internal body organs. However, its usefulness is diminished by signal dependent noise known as speckle noise. Speckle noise degrades target detectability in ultrasound images and reduces contrast and resolution, affecting the ability to identify normal and pathological tissue. For accurate diagnosis, it is important to remove this noise from ultrasound images. In this study, a new filtering technique is proposed for removing speckle noise from medical ultrasound images. It is based on Gabor filtering. Specifically, a preprocessing step is added before applying the Gabor filter. The proposed technique is applied to various ultrasound images, and certain measurement indexes are calculated, such as signal to noise ratio, peak signal to noise ratio, structure similarity index, and root mean square error, which are used for comparison. In particular, five widely used image enhancement techniques were applied to three types of ultrasound images (kidney, abdomen and ortho). The main objective of image enhancement is to obtain a highly detailed image, and in that respect, the proposed technique proved superior to other widely used filters.
Decision Tree Classifiers to determine the patient’s Post-operative Recovery ...Waqas Tariq
Machine Learning aims to generate classifying expressions simple enough to be understood easily by the human. There are many machine learning approaches available for classification. Among which decision tree learning is one of the most popular classification algorithms. In this paper we propose a systematic approach based on decision tree which is used to automatically determine the patient’s post–operative recovery status. Decision Tree structures are constructed, using data mining methods and then are used to classify discharge decisions.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
This document presents a hybrid algorithm using biogeography-based optimization (BBO) and ant colony optimization (ACO) for land cover feature extraction from remote sensing images. The algorithm first analyzes a training image to identify features that BBO and ACO classify efficiently. It then applies BBO to clusters containing these features and ACO to remaining clusters. An evaluation shows the hybrid algorithm achieves a higher kappa coefficient of 0.97 compared to 0.67 for BBO alone, indicating better classification accuracy. The authors conclude the algorithm effectively handles uncertainties in remote sensing images and future work could improve efficiency further.
Image classification, remote sensing, P K MANIP.K. Mani
Image classification involves using spectral bands of images to separate landscape features into categories. Pixels with similar spectral signatures are clustered and classified using techniques like maximum likelihood classification. This results in a classified image map where each pixel is assigned a land cover class. However, classified maps have errors, so accuracy assessment is important to estimate the map's accuracy. Supervised classification involves using training areas of known land cover to develop spectral signatures for classification, while unsupervised classification clusters pixels without prior class definitions.
Artificial Neural Networks Lect2: Neurobiology & Architectures of ANNSMohammed Bennamoun
This document discusses the structure and function of biological neurons and artificial neural networks (ANNs). It covers topics such as:
- The basic components of biological neurons including the cell body, dendrites, axon, and synapses.
- Models of artificial neurons including linear and nonlinear activation functions.
- Different types of neural network architectures including feedforward, recurrent, and feedback networks.
- Training algorithms for ANNs including supervised and unsupervised learning methods. Weights are modified to minimize error between network outputs and training targets.
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
Applications of Digital image processing in Medical FieldAshwani Srivastava
This document discusses different types of electromagnetic radiation used for imaging. It describes digital images as composed of pixels and notes that digital image processing involves manipulating digital images on a computer. It outlines different levels of image processing from low-level tasks like noise reduction to mid-level tasks like segmentation to high-level tasks like image analysis. It provides examples of imaging applications using gamma rays, X-rays, ultraviolet light, microwaves, radio waves, and magnetic resonance imaging.
This document provides an introduction to various medical imaging modalities including X-ray, CT, mammography, MRI, PET, SPECT, and ultrasound. It discusses the principles, techniques, and indications for each modality. Key terms are defined. Images demonstrate examples of each type of imaging. The objectives are to recognize imaging study types, discuss how images are produced, list common indications, and describe imaging precaution considerations.
This document provides an introduction to medical image processing. It discusses various medical imaging modalities like X-ray, CT, MRI, ultrasound, PET, and angiography. It then describes the basic steps in a medical image processing system: acquisition, preprocessing, segmentation, detection, analysis, and diagnosis. Preprocessing techniques like filtering and denoising are discussed. The document concludes by mentioning some applications of medical image processing like compression, retrieval, and tumor detection.
This document discusses feature selection concepts and methods. It defines features as attributes that determine which class an instance belongs to. Feature selection aims to select a relevant subset of features by removing irrelevant, redundant and unnecessary data. This improves learning accuracy, model performance and interpretability. The document categorizes feature selection algorithms as filter, wrapper or embedded methods based on how they evaluate feature subsets. It also discusses concepts like feature relevance, search strategies, successor generation and evaluation measures used in feature selection algorithms.
Comparison of Feature selection methods for diagnosis of cervical cancer usin...IJERA Editor
Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the
pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist
to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is
obtained by Pap smear test. Image segmentation performed by multi-thresholding method and texture and shape
features are extracted related to cervical cancer. Feature selection is achieved using Mutual Information(MI),
Sequential Forward Search (SFS), Sequential Floating Forward Search (SFFS) and Random Subset Feature
Selection(RSFS) methods
The document describes a skin cancer detection mobile application that uses image processing and machine learning. The application analyzes skin images for characteristics of melanoma like asymmetry, border, color, diameter and texture. It trains a model using the MobileNet-v2 architecture on datasets containing thousands of images. The trained model achieves 70% accuracy in detecting melanoma and differentiating normal and abnormal skin lesions when tested on new images. The application has potential to help identify skin cancer in early stages and assist medical practitioners.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes recent work on content-based image retrieval (CBIR) techniques for medical images. It discusses several methods used for CBIR, including shape-based, texture-based, and feature selection methods. Recent CBIR works are surveyed that use approaches like support vector machines, nearest neighbor algorithms, and relevance feedback. While progress has been made, the document notes there are still research gaps around bridging the semantic gap between low-level image features and high-level concepts, and improving retrieval accuracy and efficiency.
A new approach for content-based image retrieval for medical applications usi...IJECEIAES
Content based image retrieval (CBIR) has become an important factor in medical imaging research and is obtaining a great success. More applications still need to be developed to get more powerful systems for better image similarity matching, and as a result getting better image retrieval systems. This research focuses on implementing low-level descriptors to maximize the quality of the retrieval of medical images. Such a research is supposed to set a better result in terms of image similarity matching. In this research a system that uses low-level descriptors is introduced. Three descriptors have been developed and applied in an attempt to increase the accuracy of image matching. The final results showed a qualified system in medical images retrieval specially that the low-level image descriptors have not been used yet in the image similarity matching in the medical field.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
BFO – AIS: A Framework for Medical Image Classification Using Soft Computing ...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.
Plant Leaf Disease Detection and Classification Using Image ProcessingIRJET Journal
The document summarizes a research paper on detecting and classifying plant leaf diseases using image processing techniques. It begins by discussing the importance of identifying plant diseases early. It then provides an overview of traditional identification methods and their limitations. Next, it describes how image processing can be used to extract features from leaf images and classify diseases using machine learning algorithms. The paper evaluates several studies that have achieved accuracy ranging from 80-99.8% using different approaches. It also discusses challenges like variable image quality and limited datasets, and potential solutions. Finally, it presents results showing accuracy of 95-99% for different techniques depending on the dataset and diseases studied.
An Innovative Deep Learning Framework Integrating Transfer- Learning And Extr...IRJET Journal
This paper proposes a deep learning framework that uses transfer learning and an XGBoost classifier to classify breast ultrasound images. It uses a VGG16 model pre-trained on general images to extract features from ultrasound images. These features are then classified using an XGBoost classifier. On a dataset of breast ultrasound images, the approach achieved 96.7% accuracy, and precision/recall/F-scores of 100%/96%/96% for benign images, 95%/97%/96% for malignant images, and 95%/98%/97% for normal images, outperforming other automatic image classification methods.
IRJET- Review of Detection of Brain Tumor Segmentation using MATLABIRJET Journal
This document provides a review of techniques for detecting brain tumors using MRI images and MATLAB. It discusses several past studies that used techniques like image enhancement, segmentation, feature extraction and machine learning classification to identify tumors. The review indicates that deep learning approaches show promise for developing an accurate, automated brain tumor detection system. It also motivates the need for such a system to help diagnose tumors early and improve treatment outcomes.
IRJET- Detection of Breast Cancer using Machine Learning TechniquesIRJET Journal
The document discusses using machine learning techniques to detect breast cancer. It begins with an introduction to machine learning and data mining, and how they can be applied to medical diagnosis like cancer detection. The study uses different machine learning classifiers and the Wisconsin Breast Cancer Dataset to classify cancers as benign or malignant. The classifiers tested were J48 and LMT, and the best accuracy obtained was 97.36% without using feature filters on the data.
A MODIFIED BINARY PSO BASED FEATURE SELECTION FOR AUTOMATIC LESION DETECTION ...ijcsit
This document summarizes a research paper that proposes a modified binary particle swarm optimization (MBPSO) method for feature selection to improve the classification of lesions in mammograms as benign or malignant. The MBPSO method incorporates iteration best into the velocity update calculation and selects solutions with fewer features when fitness values are equal. A total of 117 shape, texture, and histogram features are extracted from mammogram regions of interest. MBPSO is used to select an optimal feature subset, which is then used to train a neural network classifier. Experimental results show MBPSO obtains better classification accuracy than using the full feature set, and performs comparably to other feature selection techniques.
ABSTRACT
This paper presents an effective feature selection method that can be applied to build a computer aided diagnosis system for breast cancer in order to discriminate between healthy, benign and malignant parenchyma. Determining the optimal feature set from a large set of original features is an important preprocessing step which removes irrelevant and redundant features and thus improves computational efficiency, classification accuracy and also simplifies the classifier structure. A modified binary particle swarm optimized feature selection method (MBPSO)has been proposed where k-Nearest Neighbour algorithm with leave-one-out cross validation serves as the fitness function. Digital mammograms obtained from Regional Cancer Centre, Thiruvananthapuram and the mammograms from web accessible mini-MIAS database has been used as the dataset for this experiment. Region of interests from the mammograms are automatically detected and segmented. A total of 117 shape, texture and histogram features are extracted from the ROIs. Significant features are selected using the proposed feature selection method.Classification is performed using feed forward artificial neural networks with back propagation learning. Receiver operating characteristics (ROC) and confusion matrix are used to evaluate the performance. Experimental results show that the modified binary PSO feature selection method not only obtains better classification accuracy but also simplifies the classification process as compared to full set of features. The performance of the modified BPSO is found to be at par with other widely used feature selection techniques.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Influence Analysis of Image Feature Selection TechniquesOver Deep Learning ModelIRJET Journal
This document discusses using different image feature selection techniques and their impact on deep learning models for image classification. It analyzes shape, color, texture, and combined features extracted from images using techniques like local binary patterns (LBP), grid color moments, and Sobel operators. A convolutional neural network (CNN) is used as the deep learning classifier. The performance is evaluated on a diabetic retinopathy detection dataset in terms of classification accuracy. The goal is to determine which feature selection techniques improve accuracy while minimizing computational resources when used with CNNs. A system is proposed that extracts individual features and combined features from images, then classifies them using CNNs to compare the impact of different feature selection approaches.
1. The document proposes a system for searching similar sub-regions in medical images to help medical practitioners and researchers.
2. The key components of the system include collecting medical images, preprocessing them by transforming formats and sizes, storing images and extracting features to enable rapid search, and returning search results to users.
3. The document discusses challenges in building such a system and outlines approaches for image transformation, feature extraction, indexing images to support fast search, and algorithms that can be used to match sub-regions between images.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
IRJET- Brain Tumor Detection and Classification with Feed Forward Back Propag...IRJET Journal
This document presents a method for detecting and classifying brain tumors in MRI images using a feed forward back propagation neural network. It first preprocesses MRI images by dividing them into blocks and applying Haar transforms for noise removal and edge preservation. Statistical, GLCM, morphological and edge features are then extracted from each block. These features are used to identify abnormal areas. The blocks are then classified as normal or tumor using a feed forward back propagation neural network, which can model nonlinear relationships and is trained to reduce error rates. The method achieves 98% classification accuracy on a benchmark MRI dataset. It results in high accuracy tumor detection with less iterations, reducing computation time compared to previous methods.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
Similar to Exploratory Analysis of Feature Selection Techniques in Medical Image Processing (20)
Exploring the Experiences of Gender-Based Violence
and The Associated Psychosocial and Mental Health
Issues of Filipino HIV-Positives: Implications for
Psychological Practice
Evangeline R Castronuevo-Ruga1, Normita A Atrillano2
Abstract: The phenomenon of gender-based violence has generated attention from research practitioners and helping professionals since
the surge of the women’s movement three or so decades ago in the Philippines. At about the same time, the HIV-AIDS gained similar
attention with the disclosure of the first ever case of the country in the mid-80s. Only recently, however, has the intersectionality of these
two phenomena been looked into by the research community in other countries and has yet to see parallel response locally. This research,
therefore, attempts to map out the lived experiences of People Living with Human Immuno Deficiency Virus (PLHIV) who have undergone
gender-based violence (GBV). It specially looks into the consequent psychosocial and mental health issues. Using focus group discussion with
24 purposively sampled participants from the highly vulnerable groups based in three major Philippine cities, thematic analysis reveals that
the participants experienced various forms of gender-based violence, e.g., sexual, emotional/psychological, economic, verbal, physical) and
expressions of stigma and discrimination, which in turn, led to manifestations of different emotional and psychological trauma, depression,
internalized homophobia, greater health risks and risk-taking behaviours, among others. It might be worthwhile to consider the possibility
that the consequent risk-taking and self-injurious tendencies played a role in their eventual contraction of HIV.
Estimation of Storage-Draft Rate Characteristics of
Rivers in Selangor Region
Farah Syazana Abd Latif1, Siti Fatin Mohd Razali2
1,2Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia
Abstract: Drought is a phenomenon of extreme water shortage that has significant economic, social, environmental and human life
impact. Streamflow drought characteristics and properties are useful in the design of hydro-technical projects, water resources planning and
management purposes. Information on low flow magnitude, frequency, probability and return period are very crucial in analysing
streamflow drought at the operational level in public water supply. The objectives of this study are to determine the characteristics of low
flow for every streamflow station in the Selangor region. The estimation of minimum storage draft-rate with the probability of low flow
return periods of 2, 5, 10, 20, and 50 years is presented in this paper.
Awwal-Awwal Tampat Budjang Journey Back to
Pre-Islamic Epoch: A Cultural Semiotic
Helen G Juaini1
Abstract: Cultural background plays a significant role in the sphere of semiotics. Semiotics as a discipline is recognized as a useful tool in
gauging cultural background and identifying signs that might represent the message of a certain work. Given the rich cultural context of
Tawi-Tawi oral literature this can be used in studying semiotics. Semiotic tools were employed to interpret the awwal-awwal as provided by
the respondents and to formulate a subsequent understanding of this oral literature in relation to the Sama’s claim of sacredness of Tampat
Budjang.
Politeness and Intimacy in Application Letters of
Three Cultural Groups in Mindanao
Helen G Juaini1
Abstract: 150 application letters from the three cultural groups in Mindano, namely Sinama, Subanen, and Tausug have been analysed
in a mixed-method design. The focus of the study is on the two features of politeness and intimacy. In the quantitative analysis, the model
proposed by Brown & Levinson (1987) and that of Columns (2005) which have drawn upon the features of indirectness in requesting and
the length of letters as the indicators of politeness are used. In the qualitative and descriptive analysis formality in salutation and opening
clause as well as the use of abbreviated forms are taken into account. The result shows that Tausug use the politest style in their application
letters, followed by Sinama and Subanen respectively. On the other hand, Sinama, Subanen, and Tausug use the least intimate style in their
business letters. The findings are hoped to help better inter-cultural understanding, especially with respect to written rhetorical
characteristics.
New Authentication Algorithm for IoT Environment
based on Non-Commutative Algebra and Its
Implementation
Maki Kihara1, Satoshi Iriyama2
1,2Tokyo University of Science
Abstract: Recently, IoT devices such as robots, speakers, domestic electrical appliances and smart devices are provided everywhere for
everyone. While their authentication request is quite ubiquitously, namely, an authentication for sharing services, the actual
implementations are patchy schemes of variety security policies. In this study, we propose the new authentication scheme for IoT devices
without certificate authority which is fast enough as well as secure. The verification algorithm is based on suitable ciphered metric. We
define a class of such verifiable encryption and give an example for authentication. Moreover, we show the implementation which keeps
perfect secrecy by means of Shannon’s theory.
Developing a Strategic Organisational Learning
Framework to Improve Caribbean Disaster
Management Performance
Joanne Persad1
Abstract: Disasters are social constructs and require an agility and adaptability from national disaster organisations (NDOs). The
environment in which NDOs operate are complex adaptive systems environment, and organisational learning as a key approach is considered
fundamental to strengthening the ability of an NDO to perform at its best. With the potential for loss of lives, the destruction of critical
infrastructure and housing and to the risk of setting back a country’s economic development by many years, learning from the lessons of the
past, to reduce the negative impacts is critical for the onward growth of Caribbean countries which, for the most part, are small island
developing states. The Caribbean Region is the one of the most hazard prone regions in the world (Walbrent College 2012). Lessons from
disaster impacts are identified, gaps are well documented, and failures are sometimes exposed. But learning, in terms of making changes to
improve systems, performance and resilience, is questionable. The lessons must be applied for change to occur, this is part of the knowledge
management process in the context of disaster organisations. The purpose of this study is to explore the apparent inability of national
disaster organizations in the Caribbean to apply the lessons learnt from previous disasters. Three (3) Caribbean countries have been selected
for this research. It is a multiple case study where the unit of analysis is the national disaster organisation. This study is based on an
interpretive paradigm.
Combating Climate Change and Land Degradation in
The West African Sahel: A Multi-Country Study of
Mali, Niger and Senegal
S A Igbatayo1
1Head, Department of Economics & Management Studies, AFE Babalola University, Nigeria
Abstract: The West African Sahel is a vast ecological zone separating the Sahara Desert to the north and Sudanian savannah to the
south; traversing Senegal, Mali, Burkina Faso, Niger, northern Nigeria and Chad. With a population estimated at more than 60 million
people, the region features a multiplicity of development challenges. It is home to some of the world’s most impoverished people, whose
livelihoods are mostly reliant on rain-fed agriculture. Characterized by semi-arid vegetation, the West African Sahel is one of the most
environmentally degraded ecosystems in the world. The region faces severe and recurring bouts of droughts since the 1980s, jeopardizing
environmental sustainability. During the past four decades, the West African Sahel has witnessed below-average annual precipitation, with
two severe drought periods in 1972-1973 and 1983–1984, in a development that undermined agricultural productivity and spawned
severe land degradation. Various studies have predicted even more severe climate variability and change in the region, with drier and more
frequent dry periods expected. The intergovernmental Panel on climate change (IPCC, 2007) revealed a decline in annual rainfall in West
Africa since the end of the 1960s, with a reduction of 20% to 40% observed in the periods 1931-1960 and 1968–1990. Repeated
droughts, fuelled by climate change, have undermined land productivity, turning arable soils into marginal lands, and rendering land
resources vulnerable to such anthropogenic activities as over-grazing, agricultural intensification and deforestation, which are common
practices across the region. The major objective of this paper is to shed light on climate change and land degradation patterns in the West
African Sahel. It employs empirical data to analyse the trends, with particular emphasis on Mali, Niger and Senegal. The study reveals
considerable threats posed by the twin scourges of climate change and land degradation to food security, environmental sustainability and
regional stability.
Combating Climate Change and Land Degradation in
The West African Sahel: A Multi-Country Study of
Mali, Niger and Senegal
S A Igbatayo1
1Head, Department of Economics & Management Studies, AFE Babalola University, Nigeria
Abstract: The West African Sahel is a vast ecological zone separating the Sahara Desert to the north and Sudanian savannah to the
south; traversing Senegal, Mali, Burkina Faso, Niger, northern Nigeria and Chad. With a population estimated at more than 60 million
people, the region features a multiplicity of development challenges. It is home to some of the world’s most impoverished people, whose
livelihoods are mostly reliant on rain-fed agriculture. Characterized by semi-arid vegetation, the West African Sahel is one of the most
environmentally degraded ecosystems in the world. The region faces severe and recurring bouts of droughts since the 1980s, jeopardizing
environmental sustainability. During the past four decades, the West African Sahel has witnessed below-average annual precipitation, with
two severe drought periods in 1972-1973 and 1983–1984, in a development that undermined agricultural productivity and spawned
severe land degradation. Various studies have predicted even more severe climate variability and change in the region, with drier and more
frequent dry periods expected. The intergovernmental Panel on climate change (IPCC, 2007) revealed a decline in annual rainfall in West
Africa since the end of the 1960s, with a reduction of 20% to 40% observed in the periods 1931-1960 and 1968–1990. Repeated
droughts, fuelled by climate change, have undermined land productivity, turning arable soils into marginal lands, and rendering land
resources vulnerable to such anthropogenic activities as over-grazing, agricultural intensification and deforestation, which are common
practices across the region. The major objective of this paper is to shed light on climate change and land degradation patterns in the West
African Sahel. It employs empirical data to analyse the trends, with particular emphasis on Mali, Niger and Senegal. The study reveals
considerable threats posed by the twin scourges of climate change and land degradation to food security, environmental sustainability and
regional stability. It also presents a policy framework underpinned by climate change mitigation and adaptation strategies, formalizing land
rights for farmers, subsidizing farm inputs, creating grazing reserves for pastoralists and deepening poverty reduction strategies.
A Study on Factor Affecting Textile
Entrepreneurship – A Special Emphasis on Tirupur
District
P Anbuoli1
1Assistant Professor, Department of Business Administration, Mannar Thirumalai Naicker College, India
Abstract: Entrepreneurial success depends on various factors associated with the business, the entrepreneurs’ wishes to start. Entrepreneurs
need some sort of inspirations to succeed in their business ventures. Being a versatile industry, textile attracts many entrepreneurs both urban
and rural peoples and requires minimal investment to start. Textile entrepreneurs have to face several challenges and prospects associated
with their business. This study has been commenced with the objectives to check demographic profile, factors affecting textile entrepreneurs,
encouragement of external factors and personal reason behind to become textile business entrepreneurs. This study has been carried out with
100 textile entrepreneurs; the sample has been selected by using simple random sampling. This study is also carried out with non-disguised
and structured questionnaire; which consists of four parts with seeking information on demographic profile, factors affecting textile
entrepreneurs, external encouraging factors and personal reason to become textile entrepreneurs. This study uses percentage analysis, factor
analysis, Garrett score ranking, and t-test to analyse the data collected. It was concluded that textile entrepreneurs have been encouraged by
various factors and moreover several factors significantly affect their business.
Factors Affecting Consumer Purchase Behaviour
towards Online Clothing Products in Bangladesh
T Islam1
1BRAC Business School, BRAC University, Dhaka, Bangladesh
Abstract: The online clothing businesses have seen a considerable rise in recent times, with a high and growing demand. The purpose of
this study is to determine the factors that play significant roles in creating purchase intention towards the online clothing products in
Bangladesh. Secondary research was used to build the model of customer purchase intention. A structured questionnaire was employed to
gather data and test the model. Factor analysis and regression were used to test the model. The regression model suggested that customer
purchase intention was induced most by the online marketing activities of the online retailers, followed by pricing strategy implemented and
sense of security provided (in that order). To understand customer purchase intentions better, it may be important to look at additional
factors or seek better measures of the constructs. The study suggests that online retailers should heavily focus on online promotions and
pricing.
Improvement Measures on Wage System of
Construction Skilled Worker in South Korea
Kun-Hyung Lee1, Byung-Uk Jo2, Kyeoung-Min Han3, Chang-Baek Son4
1,2,3Graduate, School of Architectural Engineering, Semyung University, Jecheon-si, South Korea
4Professor, Department of Architectural Engineering, Semyung University, Jecheon-si, South Korea
Abstract: Unlike other industries, the construction industry is characterized by its heavy dependence on labour force with most work done
by workers. Still, the industry is witnessing the declining influx of young workers and the rising turnover rates of skilled workers due to such
issues as the advancement of 3D industry, negative image and absence of an established wage system. Hence, this paper proposes an
alternative scheme that would help improve the wage system and work environment for skilled construction workers in Korea.
Mastering the Recycling of Masonry while building
Tadao Ando’s Private Gallery in Lincoln Park,
Chicago
Daniel Joseph Whittaker1
Abstract: The notion of a great presence of masonry rarely conjures up the likes of buildings by master architect, Tadao Ando san of
Osaka, Japan, who is better known for his sublime shaping of space with planar forms of site-cast concrete. Perhaps though, one may recall
the ‘historical intervention’ on a grand scale—the now nine-year-old Punta Della Dogan a project (2009) in Venice, Italy, as prima facie
evidence of his dialogue with a vast quantity of ancient masonry in the Laguna. However, a new project by Ando, recently opened in
Chicago, Illinois (October 2018), presents the private-museum-gallery-going public with a new North American delight. Here, the senses
are able to indulge in a hybrid set of experiences shaped by masonry, concrete, and white painted plaster surfaces. This paper explores how
the modern concrete master has expanded his dynamic architectural vocabulary utilizing what is known as Chicago common brick: a soft,
Lake Michigan-sand and clay based fired brick, and incorporated it into his most recent private commission located in Lincoln Park,
Chicago.
RRI Buffer Based Energy and Computation Efficient
Cache Replacement Algorithm
Muhammad Shahid1
1Computer Science Department, National University of Computer and Emerging Sciences, Islamabad
Abstract: Energy consumption is an important factor of com-mutational power these days. Large scale energy consumption results in bad
system performance and high cost. To access frequently used data, we place it in Cache. Cache provides us opportunity to access that data in
a small time. Cache memory helps in retrieving data in minimum time improving the system performance and reducing power consumption.
Due to limited size of Cache, replacement algorithms used to make space for new data. There are many existing cache replacement
algorithms for example LRU, LFU, MRU, FIFO etc. Existing algorithms consume a lot of energy while replacing cold blocks of data.
Replacement algorithms are usually designed to reduce miss rate and increase hit rate. These algorithms replace cold blocks (not going to use
in future) and due to large number of cold blocks, they consume lot of energy. This paper proposes an energy and computation efficient cache
replacement algorithm that put only hot blocks in action instead of removing cold blocks. This paper also discusses different replacement
algorithms proposed in different papers and compare these algorithms on basis of different parameters mainly energy consumption. In our
experiments we have found LRU and FIFO as best replacement algorithms for Increased hit rates and Energy efficiency respectively.
Key Performance Index of Increasing Air Quality
with Construction Schedule Control
Hyoung-Chul Lim1, Dongheon Lee2, Dong-Eun Lee3, Daeyoung Kim4
1Professor, 2Doctorial Course, School of Architectural Engineering, Changwon National University, Korea
3Professor, School of Architecture & Civil Engineering, Kyungpook National University, Korea
4Professor, Department of Architecture, Kyungnam University, Korea
Abstract: Recently, air quality in residential spaces has been major concern. In particular, the indoor air quality of residential facility
before occupancy, which is related to the interior material, is a serious problem. existing research has mainly focused on pollution control
after construction, but this research has derived I key performance index I about increasing air quality and priority of management with a
controlling schedule. That is the objectives of research. The results show the relative priority of the four major items in wall‐based apartment
buildings and in column‐based apartment buildings. An analysis of the parties responsible for improvement based on the IAQ results shows
more efforts to improve IAQ are needed in material factories and engineering/design companies.
Exploring Revitalization Solutions: Engaging
Community through Media Architecture
Behzad Shojaedingivi1
1University of Tehran
Abstract: This paper aims to investigate Media Architecture and its potentials for culturally based revitalization. Media Architecture
presents a new approach based on Augmentation concepts, in which projects are designed and implemented adopting contemporary mediums
in an aesthetic way in order to attract the presence of a more cultural audience and increase the participation of the local residents.
Ultimately this will lead to an increase of interaction between different classes in neglected areas and strengthen their connection to their
built environment. This is an interdisciplinary approach in which architecture and contemporary mediums are combined aesthetically with
the aim of creating revival solutions in neglected areas.
Criteria of Creating Social Interaction for Green
Open Space in Karkh, Iraq
Sarah Abdulkareem Salih1, Sumarni Ismail2
1Master Student, 2Lecturer, Department of Architecture, Universiti Putra Malaysia, Malaysia
Abstract: This paper outlines the issue on open spaces, which led to decrease social interaction among residents in Baghdad city
nowadays. The main objective of the paper is to identify the criteria of green open spaces to achieve sound social interaction in Baghdad city,
Iraq. This paper employed quantitative method, in the form of survey, for data collection. Data were obtained from questionnaires, through
the selection of 270 respondents in a single-stage random procedure from ten specific neighbourhoods in Karkh district. The study findings
confirm that open spaces and parks is essential to enhance social interaction by implementing appropriate criteria in that open spaces or
parks. The results of this study are useful reference for urban and landscape planners, architects, social psychologists, the Municipality of
Baghdad, and researchers in this field.
This document provides information about the CoreConferences 2019 conference including:
- The conference was held from March 20-21, 2019 in Taipei, Taiwan and covered topics in architecture, business, climate change, cyber security, education, flood risk management, language teaching, universities, and women's studies.
- It was organized by Core Conferences LLC in collaboration with the Association of Scientists, Developers and Faculties.
- The editor-in-chief was Dr. A Senthilkumar and the conference was aimed at allowing academics and professionals to discuss recent progress in related fields.
ICCOTWT 2018 will be the most comprehensive conference focused on the various aspects of Cloud of Things and Wearable Technologies. This Conference provides a chance for academic and industry professionals to discuss recent progress in the area of Cloud of Things and Wearable Technologies. Furthermore, we expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject.
The goal of this conference is to bring together the researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects of Cloud of Things and Wearable Technologies.
The International Conference on Computer, Engineering, Law, Education and Management (ICCELEM 2017)” held on 28 - 29th September 2017, in collaboration with Association of Scientists, Developers and Faculties (ASDF), an International body, at The Westin Chosun Seoul, Seoul, South Korea.
The Third International Conference on “Systems, Science, Control, Communication, Engineering and Technology (ICSSCCET 2017)” held on 16 - 17th February 2017, in collaboration with Association of Scientists, Developers and Faculties (ASDF), an International body, at Teegala Krishna Reddy Engineering College, Hyderabad, India, Asia.
The First International Conference on “Advanced Innovations in Engineering and Technology (ICAIET 2017)” held on 14th - 15th Feb 2017, in collaboration with Association of Scientists, Developers and Faculties (ASDF), an International body, at Rohini College of Engineering and Technology, Tamilnadu, India, Asia.
The First International Conference on “Intelligent Computing and Systems (ICICS 2017)” held on 13th - 14th February 2017, in collaboration with Association of Scientists, Developers and Faculties (ASDF), an International body, at NSN College of Engineering and Technology, Karur, Tamilnadu, India, Asia.
The First International Conference on “Advances & Challenges in Interdisciplinary Engineering and Management 2017 (ICACIEM 2017)” held on 11 – 12th February 2017, in collaboration with Association of Scientists, Developers and Faculties (ASDF), an International body, at Vidyaa Vikas College of Engineering and Technology, Tiruchengode, Tamilnadu, India, Asia.
Wireless sensor networks can provide low cost solution accompanied with limited storage, computational capability and power for verity of real-world problems and become essential factor when sensor nodes are arbitrarily deployed in a hostile environment. The cluster head selection technique is also one of the good approaches to reduce energy consumption in wireless sensor networks. The lifetime of wireless sensor networks is extended by using the uniform cluster head selection and balancing the network loading among the clusters. We have reviewed various energy efficient schemes apply in WSNs of which we concentrated on selection of cluster head approach and proposed an new method called Sleep Scheduling Routing with in clusters for Energy Efficient [SSREE]in which some nodes in clusters are usually put to sleep to conserve energy, and this helps to prolong the network lifetime. EASSR selects a node as a cluster head if its residual energy is more than system average energy and have less energy consumption rate in previous round. Then, an Performance analysis and compared statistic results of SSREE shows of the significant improvement over existing protocol LEACH, SEP and M-GEAR protocol in terms of lifetime of network and data units gathered at BS.
Due to rapid urbanization the manufacturing processes of conventional building materials pollutes air, water and land. Hence in order to fulfil the increasing demand it is required to adopt a cost effective, eco-friendly technologies by improving the traditional techniques with the usage of available local materials. Agro – industrial and other solid waste disposal is another serious issue of concern in most of developing countries. The present paper explores the potential application of agro-waste as an ingredient for alternate sustainable construction materials.
There has been an ever-increasing interest in big data due to its rapid growth and since it covers diverse areas of applications. Hence, there seems to be a need for an analytical review of recent developments in the big data technology. This paper aims to provide a comprehensive review of the big data state of the art, conceptual explorations, major benefits, and research challenging aspects. In addition to that, several future directions for big data research are highlighted.
A correct node operation and power administration are significant issues in the wireless sensor network system. Ultrasonic, dead reckoning, and radio frequency information is obtained by using localization mechanism and worked through a specific filter algorithm. In this paper, a well-organized grid deployment method is applied to split the nodes into multiple individual grids. The tiny grids are used for improved resolution and bigger grids are used to decrease the complexity of processing. The efficiency of each grid is obtained by environmental factors such as redeployed nodes, boundaries, and obstacles. To decrease the power usage, asynchronous power management method is designed. In network communication, power management method is applied by using an asynchronous awakening scheme and n-duplicate coverage algorithm is engineered for the coverage of nodes.
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2. International Conference on Information Engineering, Management and Security 2016 (ICIEMS 2016) 34
Cite this article as: Kokula Krishna Hari Kunasekaran, Rajkumar Sugumaran. “Exploratory Analysis of Feature
Selection Techniques in Medical Image Processing”. International Conference on Information Engineering,
Management and Security 2016: 33-37. Print.
Fig 1. Process Flow in Medical Image processing
III. Feature Selection in Medical Image Processing
Feature selection is a dimensionality reduction technique widely used for data mining and knowledge discovery and it allows exclusion
of redundant features, concomitantly retaining the underlying hidden information, feature selection entails less data transmission and
efficient data mining. It also brings potential communication advantages in terms of packet collisions, data rate, and storage [4].
Feature selection is one of the key topics in machine learning and other related fields. It can eliminate the irrelevant noisy features and
thus improve the quality of the data set and the performance of learning systems [5]. Expeditious growth of digital image databases
motivated Content Based Image Retrieval (CBIR) which in turn requires efficient search schemes. Low level visual features including
color, texture and shape, are automatically selected to represent images [6].
A. Fundamental Feature Selection Techniques in Medical Image Processing
The feature selection method discussed on three steps when selecting image which are: screening, ranking and selecting. In screening,
it removes insignificant and problematic predictors and records or cases, such as predictors with too many missing values or predictors
with too much or too little variation to be useful. Ranking, Sorts remaining predictors and assigns ranks based on importance.
Selecting: It identifies the subset of features by preserving only the most significant predictors and filtering or excluding all others [7].
The Feature Selection screens, ranks, and selects are the predictors that are most significant.
B. Survey on Feature Selection Techniques
Haleh and Kenneth describes part of a larger attempt to apply machine learning techniques to such problems in an effort to
automatically generate and progress the classification rules needed for various recognition tasks, image recognition presents a diversity
of difficult classification problems involving the identification of significant scene components in the presence of noise, adopting
lighting conditions, and shifting viewpoints [8]. Since each feature used as part of a classification procedure can increase the cost and
running time of a recognition system, there is strong motivation within the image processing community to design and implement
systems with small feature sets. At the same time there is a potentially opposing need to include a sufficient set of features to achieve
high recognition rates under difficult conditions. This has led to the development of a variety of techniques within the image processing
community for finding an "optimal" subset of features from a larger set of possible features. Sérgio et al., described the advantage of a
single- valued functions that evaluate rankings to develop a family of feature selection methods based on the genetic algorithm, it
improve the accuracy of content-based image retrieval systems and it also evaluate the ranking quality allows improving retrieval
performance [9]. Medical images play a central role in patient diagnosis, therapy, surgical planning, medical reference, and training.
With the recent boom in the availability of filmless radiology equipment, the management of digital medical mages is receiving more
and more attention. Picture Archiving and Communication Systems (PACS) have been successfully introduced in many hospitals and
specialized clinics, providing quick access to screening exams and integrating the actors involved in the enterprise's workflow. The
3. International Conference on Information Engineering, Management and Security 2016 (ICIEMS 2016) 35
Cite this article as: Kokula Krishna Hari Kunasekaran, Rajkumar Sugumaran. “Exploratory Analysis of Feature
Selection Techniques in Medical Image Processing”. International Conference on Information Engineering,
Management and Security 2016: 33-37. Print.
radiological databases originally built for storing digital images have evolved from simple storage servers of past exams, kept for legal
reasons, to active and easily accessible repositories for research and decision support. Jaba and Shanthi reviewed previously on
continuous feature discretization and identified defining characteristics of the methods. Then suggest a new supervised approach which
merges discretization and feature selection to select the most relevant features which can be used for classification purpose. The
classification method to be used is Associative Classifiers [10]. Medical images are a primary part of medical diagnosis and treatment.
These images are unlike from typical photographic images primarily as they disclose internal anatomy as contrasting to an image of
surfaces. Sasi and Kumaraswamy, said with various techniques proposed in literature for feature extraction, classification and retrieval,
Content-based image retrieval (CBIR) is a widely researched area. Also discussed that Information Gain is used to achieve the structure
of a feature sets to find a subset of the original feature vector for efficient computation and features are optimized using Particle Swarm
Optimization (PSO) [11]. Yong Fan, et al., presented a framework for brain classification based on multi-parametric medical images,
and described the method advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction
by using a regional feature extraction method which takes into account joint correlations among different image parameters [12]. Ling-
Chen et al., discussed a feature selection algorithm rooted on ant colony optimization (ACO), and said Image feature selection (FS) is a
significant task which can affect the presentation of image classification and recognition [13]. Ant colony optimization (ACO) is an
evolution simulation algorithm proposed by Dorigo et al., It has been successfully used for system fault detecting, job-shop scheduling,
network load balancing, graph coloring, robotics and other combinational optimization problems. Pushpalata and Jyoti, described
feature selection technique and an ensemble model proposed to improve classification accuracy. Feature selection technique is used for
selecting subset of relevant features from the data set to build robust learning models and discussed furthermore that Classification
accuracy is improved by removing most irrelevant and redundant features from the dataset and stated that Ensemble model is proposed
for improving classification accuracy by combining the prediction of multiple classifiers, Three decision tree data mining classifiers
were considered for classification which are CART, CHAID and QUEST [7]. Jin Yu et al., presented an approach that involves the
analysis of Co focal Scanning Laser Tomography (CSLT) images using moment techniques to obtain abstract image defining features,
and then the use of these features to train classifiers for automatically differentiating CSLT images of healthy and diseased optic nerves,
and exploration in feature subset selection methods for reducing the comparatively large input space produced by the moment
methods [14].
Vasantha et al., discussed that Breast cancer is the most common type of cancer found in women, and they proposes a image classifier
to classify the mammogram images, mammogram image is classified into normal image, benign image and malignant image. A hybrid
approach of feature selection was proposed in reduction of about 75% of the features [15]. Saravana et al., discussed about feature
selection and an efficient method for feature extraction was proposed for image retrieval process and described Content-Based Image
Retrieval as a technique that utilizes the visual content of an image to search for similar images in large scale image databases. Feature
selection and feature extraction method were the significant tasks that were considered in image retrieval process [16].
Huanzhang et al. discussed about Feature subset selection as a significant subject when training classifiers in Machine Learning (ML)
problems and illustrated the information that the complexity of the classifier parameters adjustment during training swells
exponentially with the number of features. So they introduced a novel embedded feature selection method, called ESFS, which was
simulated from the wrapper method SFS as it relies on the simple standard to add incrementally most relevant features [17]. Georgia et
al., discussed the study of investigated information theoretic approach to feature selection for computer-aided diagnosis, the approach
was based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any
assumptions about the nature of their underlying relationships. They described MI that it can potentially offer some advantages over
feature selection techniques that focus only on the linear relationships of variables [18]. Mohamed et al., discussed an approach which
was proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing
microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs and showed the efficiency of
feature selection on the CAD system, and implemented the proposed method in four stages which are [19]:
a) The region of interest (ROI) selection of 32x32 pixels size which identifies clusters of microcalcifications,
b) The feature extraction stage based on the wavelet decomposition of locally processed image (region of interest) to compute the
significant features of each cluster,
c) The feature selection stage, which select the most significant features to be used in next stage, and
d) The classification stage, which classify between normal and microcalcifications' patterns and then classify between benign and
malignant microcalcifications.
Guo-Zheng et al.discussed the feature selection methods with support vector machines which contains obtained satisfactory results,
and propose a prediction risk based on feature selection method with multiple classification support vector machines. The performance
of the projected method is compared with the earlier methods of optimal brain damage rooted feature selection methods with binary
support vector machines [4]. Shuqin et al., said feature selection techniques has been widely used in various fields and discussed a new
refined feature selection module which utilizes two-step selection method in computer-aided diagnosis (CAD) system for liver disease,
the method used was filter and wrapper method, Support Vector Machine (SVM) and Genetic Algorithm (GA) And stated that the
advantage was to show the ability of accommodating multi feature selection search strategies and combining filter and wrapper
method, especially in identifying optimal and minimal feature subsets for building the classifier [20]. Yong and Ding-gang described
4. International Conference on Information Engineering, Management and Security 2016 (ICIEMS 2016) 36
Cite this article as: Kokula Krishna Hari Kunasekaran, Rajkumar Sugumaran. “Exploratory Analysis of Feature
Selection Techniques in Medical Image Processing”. International Conference on Information Engineering,
Management and Security 2016: 33-37. Print.
feature extraction and selection are of great importance in neuro image classification for identifying informative features and reducing
feature dimensionality, which are generally implemented as two separate steps and presented an integrated feature extraction and
selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM)
based feature selection [21]. Haleh and Kenneht discussed an approach being explored to develop the usefulness of machine learning
techniques for generating classification rules for complex, real world data. An approach has been implemented and tested on difficult
texture classification problems.
Fig2. CSLT Image of optic disc.
The approach involves the use of genetic algorithms as a "front end" to traditional rule induction systems in order to identify and select
the best subset of features to be used by the rule induction system [8]. Feature Selection (FS) algorithms aim at choosing a reduced
number of features that preserves the most relevant information of the dataset. FS is usually applied as a preprocessing step in data
mining tasks by removing irrelevant or redundant features (dealing with the dimensionality issue), therefore leading to more efficient
(reducing the computational cost and the amount of memory required) and accurate classification, clustering and similarity searching
processes. Since each feature used as part of a classification procedure can increase the cost and running time of a recognition system,
there is strong motivation within the image processing community to design and implement systems with small feature sets. At the
same time there is a potentially opposing need to include a sufficient set of features to achieve high recognition rates under difficult
conditions. This has led to the development of a variety of techniques within the image processing community for finding an "optimal"
subset of features from a larger set of possible features. Images have a large number of features. It is significant to identify and extract
interesting features for a particular task in order to reduce the complexion of processing. These are attributes or portion of the image
being analyzed that is most likely to give interesting rules for that problem. Not all the attributes of an image are useful for knowledge
extraction. An image can be adequately represented using the attributes of its features.
The extraction of the features from an image can be done using a variety of image processing techniques. We localize the extraction
process to very small regions in order to ensure that we capture all areas. Feature selection helps to reduce the feature space which
improves the prediction accuracy and minimizes the computation time. This is achieved by removing irrelevant, redundant and noisy
features .i.e., it selects the subset of features that can achieve the best performance in terms of accuracy and computation time. It
performs the Dimensionality reduction. Features are generally selected by search procedures.
A number of search procedures have been proposed. Popularly used feature selection algorithms are Sequential Forward Selection
(SFS), Sequential Backward selection (SBS), Genetic Algorithm (GA) and Particle Swarm Optimization. In this work a combined
approach of Greedy stepwise method and Genetic Algorithm is proposed to select the optimal features. The selected optimal features
are considered for classification.
IV. Conclusion
From this survey, it is discovered that selection algorithm determines the authenticity of a medical image process decisions. The
selection algorithms are primarily used for the screening, ranking, and selection of the images, which are the predictors that are most
significant in removing insignificant and problematic predictors and records or cases, such as predictors with too many missing values
or predictors with too much or too little variation to be useful. In medical image processing, a robust and sophisticated method will be
necessary such that two or three of the existing selection methods can be hybridized for better performance in real time.
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