The document describes a soft-decision approach for identifying microcalcification masses in digital mammograms. A clustering algorithm is used to partition mammogram images into regions. The Coefficient of Variation (CV) of pixel intensities is then calculated for each region. Regions with higher CV values likely contain microcalcification masses. The approach was tested on mammograms from a public database, and CV values for different regions are reported in a table. Experimental results showed the method could successfully identify regions containing microcalcification masses.
Breast cancer diagnosis and recurrence prediction using machine learning tech...eSAT Journals
Abstract Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Our aim is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning techniques such as Support Vector Machine, Logistic Regression, KNN and Naive Bayes. These techniques are coded in MATLAB using UCI machine learning depository. We have compared the accuracies of different techniques and observed the results. We found SVM most suited for predictive analysis and KNN performed best for our overall methodology. Keywords: Breast Cancer, SVM, KNN, Naive Bayes, Logistic Regression, Classification.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Mass Segmentation Techniques For Lung Cancer CT Imagesrahulmonikasharma
Mass segmentation methods are commonly used nowadays in modern diagnostic centers and research centers working in the field of lung cancer detection and diagnosis. We have implemented k-means and fuzzy cluster means (FCM) techniques for mass segmentation of lung CT images. The methods were compared in terms of area, perimeter and diameter. FCM outperforms K-means in terms of better detection of lung cancer area and effective values of dimensional features of lung cancer as compared to K-means method.
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
Possibilistic Fuzzy C Means Algorithm For Mass classificaion In Digital Mammo...IJERA Editor
This document presents a methodology for classifying masses in digital mammograms using feature extraction and the Possibilistic Fuzzy C Means (PFCM) clustering algorithm and Support Vector Machine (SVM) classification. The methodology involves preprocessing mammogram images using adaptive median filtering and image enhancement to reduce noise. Features such as texture, entropy, and correlation are then extracted. PFCM clustering is used to classify pixels into abnormal and normal regions based on their features. Finally, SVM classification is applied to the extracted features to classify mammograms as normal, benign, or malignant. The methodology aims to help radiologists more accurately detect abnormalities in mammograms at an earlier stage than traditional analysis.
My own Machine Learning project - Breast Cancer PredictionGabriele Mineo
This document describes a project to classify breast cancer cell samples as benign or malignant using machine learning models. It analyzes a dataset containing characteristics of cell nuclei images from 569 breast cancer cases. The dataset has 30 variables describing features like radius, texture, and perimeter. The project aims to train models and compare their performance on accuracy, sensitivity and other metrics to identify the best model for cancer prediction. Several supervised learning algorithms will be tested including naive Bayes, logistic regression, random forest, KNN, and neural networks.
Twin support vector machine using kernel function for colorectal cancer detec...journalBEEI
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Breast cancer diagnosis and recurrence prediction using machine learning tech...eSAT Journals
Abstract Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Our aim is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning techniques such as Support Vector Machine, Logistic Regression, KNN and Naive Bayes. These techniques are coded in MATLAB using UCI machine learning depository. We have compared the accuracies of different techniques and observed the results. We found SVM most suited for predictive analysis and KNN performed best for our overall methodology. Keywords: Breast Cancer, SVM, KNN, Naive Bayes, Logistic Regression, Classification.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Mass Segmentation Techniques For Lung Cancer CT Imagesrahulmonikasharma
Mass segmentation methods are commonly used nowadays in modern diagnostic centers and research centers working in the field of lung cancer detection and diagnosis. We have implemented k-means and fuzzy cluster means (FCM) techniques for mass segmentation of lung CT images. The methods were compared in terms of area, perimeter and diameter. FCM outperforms K-means in terms of better detection of lung cancer area and effective values of dimensional features of lung cancer as compared to K-means method.
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
Possibilistic Fuzzy C Means Algorithm For Mass classificaion In Digital Mammo...IJERA Editor
This document presents a methodology for classifying masses in digital mammograms using feature extraction and the Possibilistic Fuzzy C Means (PFCM) clustering algorithm and Support Vector Machine (SVM) classification. The methodology involves preprocessing mammogram images using adaptive median filtering and image enhancement to reduce noise. Features such as texture, entropy, and correlation are then extracted. PFCM clustering is used to classify pixels into abnormal and normal regions based on their features. Finally, SVM classification is applied to the extracted features to classify mammograms as normal, benign, or malignant. The methodology aims to help radiologists more accurately detect abnormalities in mammograms at an earlier stage than traditional analysis.
My own Machine Learning project - Breast Cancer PredictionGabriele Mineo
This document describes a project to classify breast cancer cell samples as benign or malignant using machine learning models. It analyzes a dataset containing characteristics of cell nuclei images from 569 breast cancer cases. The dataset has 30 variables describing features like radius, texture, and perimeter. The project aims to train models and compare their performance on accuracy, sensitivity and other metrics to identify the best model for cancer prediction. Several supervised learning algorithms will be tested including naive Bayes, logistic regression, random forest, KNN, and neural networks.
Twin support vector machine using kernel function for colorectal cancer detec...journalBEEI
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Projek pembinaan memerlukan pengurusan yang berbeza bergantung pada saiz, lokasi, jenis struktur dan faktor-faktor lain. Rekabentuk dan kaedah pembinaan perlu mengambil kira ciri-ciri unik setiap projek untuk memastikan ia disiapkan mengikut matlamat masa, kos dan kualiti yang ditetapkan.
The document lists various entertainment and performance-related terms that need to be either unscrambled or matched to their definitions. These terms include types of performances like plays, operas, concerts, and sporting events as well as the locations where they typically take place, such as theaters, radio stations, stadiums, and streets. It then provides a series of scrambled terms to unscramble and terms to match with their corresponding definitions.
1) This document provides an overview of modern Iranian history from ancient Persia to the present, highlighting important people, events, and developments.
2) It discusses Iran's 1979 revolution that established the Islamic Republic and the Iran-Iraq war in the 1980s, as well as more recent unrest and the election of President Rouhani.
3) The document also examines engines of change within Iranian society like youth and increasing roles for women in areas like sports and politics.
This document provides an introduction to Microsoft Project (MS Project) software for project management. It explains that project managers are responsible for the success of the projects they manage and tools like MS Project can assist with resources, budgets, plans, and contracts. The document defines what project management entails, including all phases from scheduling to delivery while accounting for float. It then discusses why MS Project is useful and provides steps for creating a project in MS Project, such as setting a title, displaying it as a summary task, and defining tasks.
The Baroque period in music lasted from approximately 1600 to 1750. It originated from a Portuguese word meaning irregular pearl and originally had a negative connotation referring to excessive ornamentation. During this period, new musical styles emerged in Italy that were more expressive and focused on solo singing and melody accompanied by basso continuo. While Baroque music evolved greatly over its 150 year span, it is generally characterized by features such as expressive vocal melodies, chordal harmony dominated by major and minor scales, uniform rhythm that increased in drive over time, and sudden changes in dynamics.
I believe the concept of an “MVP” (or minimally viable product) is both one of the most powerful concepts for entrepreneurs thinking through their product strategy and also one of the most misunderstood / misused terms by entrepreneurs today. These are my opinion of the five biggest misunderstandings about MVPs.
The document summarizes the key conferences that led to Canadian Confederation:
- The Charlottetown and Quebec City conferences in 1864 laid the groundwork for a united Canada and discussed forming a federation with a central and provincial governments.
- The 72 Quebec Resolutions outlined the structure of government and terms for provinces to join Confederation.
- Despite objections from some Maritimes, the London Conference in 1866 finalized the details and received royal assent, establishing the Dominion of Canada through the British North America Act of 1867.
Este documento proporciona información sobre la esquizofrenia, incluyendo su etiología, causas, signos y síntomas, y hallazgos anatómicos. La esquizofrenia es un trastorno psicótico crónico causado por una combinación de factores genéticos y ambientales. Presenta síntomas como alucinaciones, delirios, pensamiento desorganizado y comportamiento anormal. Los estudios de imagen cerebral muestran anormalidades en las estructuras límbicas y frontales. No existe una causa única conocida, pero se
Optimized proportional integral derivative (pid) controller for the exhaust t...Ali Marzoughi
The document describes using particle swarm optimization (PSO) to optimize the parameters of a proportional-integral-derivative (PID) controller for controlling the exhaust temperature of a gas turbine system. A new performance criterion called the multipurpose performance criterion (MPPC) is proposed that allows control of overshoot, rise time, and settling time by adjusting a weighting factor. The PSO algorithm is used to optimize the PID parameters by minimizing the MPPC. Results show the PSO-PID controller optimized with MPPC performs better than a conventional PID controller at achieving optimal transient response for the gas turbine exhaust temperature control system.
The document provides an analysis of key themes in George Orwell's novel 1984, including totalitarianism, propaganda, technology, language, identity, and love. It discusses how each theme is portrayed in the novel, with a focus on how the totalitarian Party maintains complete control over citizens through extreme propaganda and surveillance, the invention of Newspeak to eliminate dissenting thoughts, and the suppression of individual identity and love.
The document provides an overview of classical music genres including the symphony, string quartet, sonata, and concerto. It notes that the symphony evolved from the Italian overture and came to feature four movements. The string quartet was created by Haydn and featured equal participation by four string players. The concerto featured a soloist playing against a full orchestra in a three-movement structure with a double exposition in the first movement.
Achieving Procurement Excellence in the Banking and Insurance IndustrySAP Ariba
The document discusses achieving procurement excellence in the banking and insurance industries. It describes market trends putting pressure on sourcing functions to reduce costs, ensure compliance, speed time to market, and focus on customer centricity. Panel speakers from ABN-AMRO Bank, Santander, and Aviva insurance discuss their procurement journeys and how adopting Ariba's solutions helped provide spend visibility, standardize processes, increase savings, and manage risk. Key lessons highlighted effective spend analysis, contract and supplier management, and the ongoing challenges of process improvement and integration.
This document discusses various musical forms including binary, ternary, rondo, and others. It explains that binary form has two sections, ternary has three, and rondo form repeats one section throughout. Letters are used to label sections, with uppercase for larger sections and lowercase for subsections. Repetition, contrast, and variation are key principles in determining musical form. Texture and melody can both be used to delineate formal sections within a piece of music.
Detection of Breast Cancer using BPN Classifier in MammogramsIRJET Journal
This document presents a method for detecting breast cancer in mammograms using a Back Propagation Network (BPN) classifier. The method involves preprocessing mammogram images, extracting Grey Level Co-occurrence Matrix (GLCM) texture features from wavelet sub-bands of the images, and training a BPN classifier on the features to classify mammograms as normal or abnormal. The BPN classifier is trained using a backpropagation algorithm to minimize error and accurately classify mammograms based on the extracted GLCM features. Experimental results found the method achieved a sensitivity of 100%, specificity of 75%, and accuracy of 90.91% for breast cancer detection and classification in mammograms.
The document presents an algorithm for enhancing digital mammographic images to aid in breast cancer detection. The algorithm uses mathematical morphology for contrast enhancement and wavelet transforms for denoising. It differentiates edge pixels from noise using wavelet-based thresholding and modified mathematical morphology. The algorithm was tested on clinical images and showed significantly improved image quality and contrast over other algorithms, as measured by a Contrast Improvement Index. Preliminary tests indicate it can meaningfully improve early breast cancer diagnosis.
Projek pembinaan memerlukan pengurusan yang berbeza bergantung pada saiz, lokasi, jenis struktur dan faktor-faktor lain. Rekabentuk dan kaedah pembinaan perlu mengambil kira ciri-ciri unik setiap projek untuk memastikan ia disiapkan mengikut matlamat masa, kos dan kualiti yang ditetapkan.
The document lists various entertainment and performance-related terms that need to be either unscrambled or matched to their definitions. These terms include types of performances like plays, operas, concerts, and sporting events as well as the locations where they typically take place, such as theaters, radio stations, stadiums, and streets. It then provides a series of scrambled terms to unscramble and terms to match with their corresponding definitions.
1) This document provides an overview of modern Iranian history from ancient Persia to the present, highlighting important people, events, and developments.
2) It discusses Iran's 1979 revolution that established the Islamic Republic and the Iran-Iraq war in the 1980s, as well as more recent unrest and the election of President Rouhani.
3) The document also examines engines of change within Iranian society like youth and increasing roles for women in areas like sports and politics.
This document provides an introduction to Microsoft Project (MS Project) software for project management. It explains that project managers are responsible for the success of the projects they manage and tools like MS Project can assist with resources, budgets, plans, and contracts. The document defines what project management entails, including all phases from scheduling to delivery while accounting for float. It then discusses why MS Project is useful and provides steps for creating a project in MS Project, such as setting a title, displaying it as a summary task, and defining tasks.
The Baroque period in music lasted from approximately 1600 to 1750. It originated from a Portuguese word meaning irregular pearl and originally had a negative connotation referring to excessive ornamentation. During this period, new musical styles emerged in Italy that were more expressive and focused on solo singing and melody accompanied by basso continuo. While Baroque music evolved greatly over its 150 year span, it is generally characterized by features such as expressive vocal melodies, chordal harmony dominated by major and minor scales, uniform rhythm that increased in drive over time, and sudden changes in dynamics.
I believe the concept of an “MVP” (or minimally viable product) is both one of the most powerful concepts for entrepreneurs thinking through their product strategy and also one of the most misunderstood / misused terms by entrepreneurs today. These are my opinion of the five biggest misunderstandings about MVPs.
The document summarizes the key conferences that led to Canadian Confederation:
- The Charlottetown and Quebec City conferences in 1864 laid the groundwork for a united Canada and discussed forming a federation with a central and provincial governments.
- The 72 Quebec Resolutions outlined the structure of government and terms for provinces to join Confederation.
- Despite objections from some Maritimes, the London Conference in 1866 finalized the details and received royal assent, establishing the Dominion of Canada through the British North America Act of 1867.
Este documento proporciona información sobre la esquizofrenia, incluyendo su etiología, causas, signos y síntomas, y hallazgos anatómicos. La esquizofrenia es un trastorno psicótico crónico causado por una combinación de factores genéticos y ambientales. Presenta síntomas como alucinaciones, delirios, pensamiento desorganizado y comportamiento anormal. Los estudios de imagen cerebral muestran anormalidades en las estructuras límbicas y frontales. No existe una causa única conocida, pero se
Optimized proportional integral derivative (pid) controller for the exhaust t...Ali Marzoughi
The document describes using particle swarm optimization (PSO) to optimize the parameters of a proportional-integral-derivative (PID) controller for controlling the exhaust temperature of a gas turbine system. A new performance criterion called the multipurpose performance criterion (MPPC) is proposed that allows control of overshoot, rise time, and settling time by adjusting a weighting factor. The PSO algorithm is used to optimize the PID parameters by minimizing the MPPC. Results show the PSO-PID controller optimized with MPPC performs better than a conventional PID controller at achieving optimal transient response for the gas turbine exhaust temperature control system.
The document provides an analysis of key themes in George Orwell's novel 1984, including totalitarianism, propaganda, technology, language, identity, and love. It discusses how each theme is portrayed in the novel, with a focus on how the totalitarian Party maintains complete control over citizens through extreme propaganda and surveillance, the invention of Newspeak to eliminate dissenting thoughts, and the suppression of individual identity and love.
The document provides an overview of classical music genres including the symphony, string quartet, sonata, and concerto. It notes that the symphony evolved from the Italian overture and came to feature four movements. The string quartet was created by Haydn and featured equal participation by four string players. The concerto featured a soloist playing against a full orchestra in a three-movement structure with a double exposition in the first movement.
Achieving Procurement Excellence in the Banking and Insurance IndustrySAP Ariba
The document discusses achieving procurement excellence in the banking and insurance industries. It describes market trends putting pressure on sourcing functions to reduce costs, ensure compliance, speed time to market, and focus on customer centricity. Panel speakers from ABN-AMRO Bank, Santander, and Aviva insurance discuss their procurement journeys and how adopting Ariba's solutions helped provide spend visibility, standardize processes, increase savings, and manage risk. Key lessons highlighted effective spend analysis, contract and supplier management, and the ongoing challenges of process improvement and integration.
This document discusses various musical forms including binary, ternary, rondo, and others. It explains that binary form has two sections, ternary has three, and rondo form repeats one section throughout. Letters are used to label sections, with uppercase for larger sections and lowercase for subsections. Repetition, contrast, and variation are key principles in determining musical form. Texture and melody can both be used to delineate formal sections within a piece of music.
Detection of Breast Cancer using BPN Classifier in MammogramsIRJET Journal
This document presents a method for detecting breast cancer in mammograms using a Back Propagation Network (BPN) classifier. The method involves preprocessing mammogram images, extracting Grey Level Co-occurrence Matrix (GLCM) texture features from wavelet sub-bands of the images, and training a BPN classifier on the features to classify mammograms as normal or abnormal. The BPN classifier is trained using a backpropagation algorithm to minimize error and accurately classify mammograms based on the extracted GLCM features. Experimental results found the method achieved a sensitivity of 100%, specificity of 75%, and accuracy of 90.91% for breast cancer detection and classification in mammograms.
The document presents an algorithm for enhancing digital mammographic images to aid in breast cancer detection. The algorithm uses mathematical morphology for contrast enhancement and wavelet transforms for denoising. It differentiates edge pixels from noise using wavelet-based thresholding and modified mathematical morphology. The algorithm was tested on clinical images and showed significantly improved image quality and contrast over other algorithms, as measured by a Contrast Improvement Index. Preliminary tests indicate it can meaningfully improve early breast cancer diagnosis.
Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Ma...csandit
This document summarizes an experiment that used n-gram features extracted from mammographic images and classified the images using a neural network. Regions of interest from mammograms in the miniMIAS database were represented using n-gram models by treating pixel intensities like words. Three-gram and four-gram features were extracted and normalized. The features were input to an artificial neural network classifier to classify regions as normal or abnormal. Experiments varying n, grey levels, and background tissue showed the highest accuracy of 83.33% for classifying fatty background tissue using three-gram features reduced to 8 grey levels.
A Progressive Review on Early Stage Breast Cancer DetectionIRJET Journal
This document provides a progressive review of techniques for early stage breast cancer detection. It discusses various image segmentation methods used in previous research like threshold-based, region-based, edge-based, and clustering-based segmentation. Feature extraction techniques discussed include gray level co-occurrence matrix, principal component analysis, and linear discriminant analysis. Classifiers reviewed are convolutional neural networks, support vector machines, artificial neural networks, random forests, and decision trees. The paper analyzes the merits and limitations of these techniques and identifies convolutional neural networks and artificial neural networks as providing high accuracy for breast cancer classification from images.
A Comparative Study on the Methods Used for the Detection of Breast Cancerrahulmonikasharma
Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques.
IRJET - A Conceptual Method for Breast Tumor Classification using SHAP Values ...IRJET Journal
This document proposes a conceptual method for classifying breast tumors as benign or malignant using SHAP values and Adaboost. It involves scoring ultrasound image features using the BI-RADS lexicon with human input. SHAP value mining is used to discover diagnostic rules from the scored features. Weak classifiers are constructed by combining benign and malignant rules. The Adaboost algorithm then integrates the weak classifiers into a strong classifier for tumor classification. Experimental results on 250 patient records show the proposed method achieves high accuracy, specificity, and sensitivity, indicating potential for clinical use.
A Novel DBSCAN Approach to Identify Microcalcifications in Cancer Images with...Editor IJCATR
Cancer is the most deadly disease among the human life. Breast Cancer is one of the most common cancers in this
industrialized world and it is the most common cause of cancer related death among worldwide. Many segmentation
technologies and clustering technologies like K-Means, K-Mediod, CLARANS etc have been proposed to identify the
microcalcifications but this paper presents our new approach of identification of cancer cells in the images containing with
noise and the performance analysis
This paper presents a novel edge-based method for segmenting masses in mammogram images. The method first enhances the image contrast and reduces noise. It then computes a texture descriptor for each pixel to create an energy texture image. Edge detection is performed on this image to identify closed-path edges representing possible mass regions. The boundary of the identified mass region is used as the segmented contour of the mass. Preliminary results show the proposed method outlines masses more closely than radiologist markings.
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
CANCER CLUMPS DETECTION USING IMAGE PROCESSING BASED ON CELL COUNTINGIRJET Journal
This document describes a proposed method for detecting cancer clumps using image processing techniques including cell counting. The method involves preprocessing images using techniques like grayscaling, binarization, and edge detection. Cancer cells are then identified and segmented. Features are extracted from the segmented regions and fed into a deep learning model for classification and counting of cancer cells. The proposed approach aims to automatically detect cancer cells in images as a way to help speed up cancer research and improve accuracy over existing methods. If successfully implemented and refined with feedback, it could open new avenues for cancer cell detection in medical imaging.
Modified fuzzy rough set technique with stacked autoencoder model for magneti...IJECEIAES
Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Netwo...IRJET Journal
1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
Breast Cancer Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict breast cancer from patient data and imaging results. It first provides background on breast cancer, noting it is the most commonly diagnosed cancer worldwide. The document then reviews prior works applying machine learning to breast cancer prediction, finding support vector machines achieved the highest accuracy. It describes the dataset used, from the University of Wisconsin, containing patient data and tumor characteristics. Finally, it explores the data and discusses implementing classification algorithms like logistic regression, support vector machines, random forests and neural networks to predict cancer type, finding logistic regression achieved the highest accuracy of 98.24%.
This document reviews various machine learning techniques for detecting breast cancer. It provides a comparative analysis of algorithms like KNN, decision trees, logistic regression, SVM, ANN, Naive Bayes and others. Accuracy rates from previous studies evaluating these algorithms on breast cancer datasets are presented. KNN and SVM performed best with accuracies above 96%. The review concludes that machine learning plays an important role in early detection of breast cancer but future work could compare machine learning to deep learning approaches.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer...IRJET Journal
The document discusses using supervised machine learning algorithms to evaluate the performance of breast cancer diagnosis. It evaluates algorithms like perceptron, cascade-forward backpropagation, and feed-forward backpropagation on a breast cancer dataset from the Wisconsin Breast Cancer Diagnosis database. The algorithms are used to develop a process for diagnosis and prediction of breast cancer that could help physicians diagnose the disease more accurately.
Logistic Regression Model for Predicting the Malignancy of Breast CancerIRJET Journal
1. The document describes using a logistic regression machine learning model to predict whether breast cancer is benign or malignant based on characteristics of breast cell samples.
2. The model was trained on a dataset of 569 samples described by 33 characteristics and achieved 94.94% accuracy on the training data and 92.10% accuracy on the test data.
3. The model provides a way to efficiently diagnose breast cancer and determine the appropriate treatment needed based on whether the cancer is predicted as benign or malignant.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
Similar to A Soft-Decision Approach for Microcalcification Mass Identification from Digital Mammogram. (20)
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.