The document describes an automated screening system for detecting acute skin cancer using neural networks and texture analysis. The proposed system aims to automatically detect melanoma in skin lesion images captured by smartphones with higher accuracy than existing methods. It uses techniques like texture segmentation, Gray Level Co-occurrence Matrix (GLCM) for feature extraction, and a neural network for classification. The results show the proposed system can detect melanoma in images with 97% accuracy, an increase over prior methods.
Skin Cancer Detection using Digital Image Processing and Implementation using...ijtsrd
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various pre-processing technique for noise removal and image enhancement. Then the image is undergone to segmentation using Thresholding method. Some features of image have to be extracted using ABCD rules. In this work, Asymmetry index and Geometric features are extracted from the segmented image. These features are given as the input to classifier. Artificial Neural Network ANN with feed forward architecture is used for classification purpose. It classifies the given image into cancerous or non-cancerous. The proposed algorithm has been tested on the ISIC International Skin Imaging Collaboration 2017 training and test datasets. The ground truth data of each image is available as well, so performance of this work can evaluate quantitatively. Khaing Thazin Oo | Dr. Moe Mon Myint | Dr. Khin Thuzar Win "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18751.pdf
SkinCure: An Innovative Smart Phone Based Application to Assist in Melanoma E...sipij
Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical
evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma.
Further investigations have shown that the survival rates in patients depend on the stage of the infection;
early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and
prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due
to doctors’ subjectivity. This paper proposes an innovative and fully functional smart-phone based
application to assist in melanoma early detection and prevention. The application has two major
components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a
novel equation to compute the time for skin to burn is thereby introduced. The second component is an
automated image analysis module which contains image acquisition, hair detection and exclusion, lesion
segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image
database from Pedro Hispano Hospital for development and testing purposes. The image database
contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases.
The experimental results show that the proposed system is efficient, achieving classification of the normal,
atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOMCSCJournals
Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
IRJET- Texture Feature Extraction for Classification of MelanomaIRJET Journal
This document discusses using texture feature extraction and a support vector machine classifier to classify skin images as malignant melanoma or benign. It proposes extracting gray-level co-occurrence matrix features from skin images to capture texture characteristics. These features would then be input to a support vector machine classifier trained to differentiate between melanoma and non-melanoma skin images. The goal is to develop an automated computer-aided system for early detection of malignant melanoma from digital skin images.
IRJET - Histogram Analysis for Melanoma Discrimination in Real Time ImageIRJET Journal
The document presents a novel framework for recognizing melanoma in real-time skin images using k-means clustering and support vector machine algorithms. It discusses the challenges of automated melanoma recognition due to variations in melanoma appearance and similarities to non-melanoma lesions. A two-stage approach is proposed involving lesion segmentation followed by classification using deep learning networks to extract discriminative features for accurate melanoma recognition.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
Skin Cancer Detection using Digital Image Processing and Implementation using...ijtsrd
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various pre-processing technique for noise removal and image enhancement. Then the image is undergone to segmentation using Thresholding method. Some features of image have to be extracted using ABCD rules. In this work, Asymmetry index and Geometric features are extracted from the segmented image. These features are given as the input to classifier. Artificial Neural Network ANN with feed forward architecture is used for classification purpose. It classifies the given image into cancerous or non-cancerous. The proposed algorithm has been tested on the ISIC International Skin Imaging Collaboration 2017 training and test datasets. The ground truth data of each image is available as well, so performance of this work can evaluate quantitatively. Khaing Thazin Oo | Dr. Moe Mon Myint | Dr. Khin Thuzar Win "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18751.pdf
SkinCure: An Innovative Smart Phone Based Application to Assist in Melanoma E...sipij
Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical
evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma.
Further investigations have shown that the survival rates in patients depend on the stage of the infection;
early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and
prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due
to doctors’ subjectivity. This paper proposes an innovative and fully functional smart-phone based
application to assist in melanoma early detection and prevention. The application has two major
components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a
novel equation to compute the time for skin to burn is thereby introduced. The second component is an
automated image analysis module which contains image acquisition, hair detection and exclusion, lesion
segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image
database from Pedro Hispano Hospital for development and testing purposes. The image database
contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases.
The experimental results show that the proposed system is efficient, achieving classification of the normal,
atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOMCSCJournals
Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
IRJET- Texture Feature Extraction for Classification of MelanomaIRJET Journal
This document discusses using texture feature extraction and a support vector machine classifier to classify skin images as malignant melanoma or benign. It proposes extracting gray-level co-occurrence matrix features from skin images to capture texture characteristics. These features would then be input to a support vector machine classifier trained to differentiate between melanoma and non-melanoma skin images. The goal is to develop an automated computer-aided system for early detection of malignant melanoma from digital skin images.
IRJET - Histogram Analysis for Melanoma Discrimination in Real Time ImageIRJET Journal
The document presents a novel framework for recognizing melanoma in real-time skin images using k-means clustering and support vector machine algorithms. It discusses the challenges of automated melanoma recognition due to variations in melanoma appearance and similarities to non-melanoma lesions. A two-stage approach is proposed involving lesion segmentation followed by classification using deep learning networks to extract discriminative features for accurate melanoma recognition.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
IRJET- Skin Disease Detection using Image Processing with Data Mining and Dee...IRJET Journal
This document presents a skin disease detection method using image processing, data mining, and deep learning techniques. The proposed system uses a mobile application where users can upload images of affected skin areas. The images then undergo preprocessing like filtering and segmentation. Features are extracted from the images using techniques like 2D wavelet transform and GLCM. These features are classified using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that CNN achieves higher overall accuracy compared to SVM, with accuracies of 99.1% for CNN vs 90.7% for SVM.
IRJET- Analysis of Skin Cancer using ABCD TechniqueIRJET Journal
This document describes a proposed method for analyzing skin cancer using the ABCD technique. It begins with an introduction to skin cancer and melanoma. The proposed method involves preprocessing the skin lesion image using filters to reduce noise, segmenting the lesion from the image, extracting features using the ABCD parameters of asymmetry, border, color, and diameter, and then identifying malignant melanoma based on the feature analysis. If melanoma is detected early using this technique, it could help reduce healthcare costs by lowering the need for biopsies. The method aims to accurately detect melanoma for early treatment when survival rates are highest.
IRJET- Skin Cancer Detection using Digital Image ProcessingIRJET Journal
This document describes research on developing a system for detecting skin cancer through digital image processing. The system uses dermoscopic images of skin lesions that are preprocessed to remove noise. Texture features are then extracted from the images using Gray Level Co-occurrence Matrices and Gabor filtering. These features are input into a support vector machine for classification of images into cancerous or non-cancerous categories. The researchers achieved an accuracy of 77% and discuss potential improvements and applications of the system to help dermatologists detect melanoma and other skin cancers at early stages.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
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.
IRJET -Malignancy Detection using Pattern Recognition and ANNSIRJET Journal
This document discusses using pattern recognition and artificial neural networks (ANNs) to detect malignancy, specifically melanoma skin cancer. It describes preprocessing dermoscopy images to remove noise, then implementing an ANN with 12 neurons in each layer to classify images as cancerous or non-cancerous based on 12 selected features. After training the ANN, it is tested on new data for decision making. The method provides efficient classification compared to alternative gradient descent approaches that may result in incorrect predictions. Publicly available skin cancer data is used to train and validate the ANN model.
InnoMela is a new medical device developed by Innovative Imaging Concepts to help dermatologists detect melanoma. Melanoma is a dangerous form of skin cancer that is increasing in cases and causes many deaths each year. InnoMela uses multispectral analysis beyond what the human eye can see to improve the accuracy of identifying suspicious lesions. This has the potential to reduce errors in diagnosis and improve early detection of melanoma when treatment is most effective.
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.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Skin cancer exposed - Background informationXplore Health
This document provides background information on skin cancer and melanoma. It discusses the state of research on melanoma, including epidemiology, causes, diagnosis, prognosis, treatment options and molecular pathways. Ethical, legal and social aspects related to prevention, screening, treatment and research are also reviewed. The document is intended to help educators prepare lessons using multimedia resources on a skin cancer module.
Skin Lesion Classification using Supervised Algorithm in Data Miningijtsrd
Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and skin lesions is crucial.J48 Algorithm and SVM SUPPORT VECTOR MACHINE based techniques to estimate effort. In this work proposed system of the project is using data mining techniques for collecting the datasets for skin cancer. So that system can overcome to diagnosing the disease quickly and accuracy. Comparing to other algorithm proposed algorithm has more accuracy. When we have to using two kind of algorithm .They are J48, SVM. J48 Algorithm produced better accuracy more than SVM algorithm. The accuracy of the proposed system is 90.2381 . It means this prediction is very close to the actual values. G. Saranya | Dr. S. M. Uma "Skin Lesion Classification using Supervised Algorithm in Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29346.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29346/skin-lesion-classification-using-supervised-algorithm-in-data-mining/g-saranya
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
Optical properties of human skin as nonmelanoma skin cancer diagnosticsmathga
The document discusses using optical properties like absorption and scattering coefficients to detect nonmelanoma skin cancers like basal cell carcinoma and squamous cell carcinoma. It analyzes ex vivo data on the optical properties of normal and cancerous skin and finds the absorption and scattering coefficients can help distinguish cancer types. While results are promising, more data is needed accounting for factors like age, gender, and skin color to improve cancer classification.
Mymmo is a digital mammogram analysis software developed by Travancore Analytics. It is a Computer Aided Detection (CAD) system that assists the diagnostician or radiologist in early detection of breast cancer.
Highly experienced engineers in the fields of medical imaging, 3D and 2D image processing and various databases have developed this highly unique and efficient diagnostic tool.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Segmentation and Classification of Skin Lesions Based on Texture FeaturesIJERA Editor
Skin cancer is the most common type of cancer and represents 50% all new cancers detected each year. The deadliest form of skin cancer is melanoma and its incidence has been rising at a rate of 3% per year. Due to the costs for dermatologists to monitor every patient, there is a need for an computerized system to evaluate a patient‘s risk of melanoma using images of their skin lesions captured using a standard digital camera. In Proposed method, a novel texture-based skin lesion segmentation algorithm is used and to classify the stages of skin cancer using probabilistic neural network. Probabilistic neural network will give better performance in this system to detect a lot of stages in skin lesion. To extract the characteristics from various skin lesions and its united features gives better classification with new approached probabilistic neural network. There are five different skin lesions commonly grouped as Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Melanocytic Nevus / Mole (ML), Squamous Cell Carcinoma (SCC), Seborrhoeic Keratosis (SK). The system will be used to classify the queried images automatically to decide the stages of abnormality. The lesion diagnosis system involves two stages of process such as training and classification. Feature selection is used in the classified framework that chooses the most relevant feature subsets at each node of the hierarchy. An automatic classifier will be used for classification based on learning with some training samples of each stage. The accuracy of the proposed neural scheme is higher in discriminating cancer and pre-malignant lesions from benign skin lesions, and it attains an total classification accuracy is high of skin lesions.
Skin cure an innovative smart phone based application to assist in melanoma e...sipij
This document proposes a smart phone application called SKINcure that aims to assist with melanoma early detection and prevention. The application has two main components: 1) a UV alert module that notifies users of sunburn risk and calculates time to burn, and 2) an image analysis module that allows users to take skin images and classifies them as normal, atypical, or melanoma with 96.3-97.5% accuracy by analyzing features like hair detection, lesion segmentation, and classification algorithms. The proposed system utilizes a dermoscopy image database containing 200 images for development and testing, achieving high accuracy in detecting different lesion types automatically.
SKIN CANCER DETECTION AND SEVERITY PREDICTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques to detect skin cancer and predict its severity. It first describes the types of skin cancer (melanoma, squamous cell carcinoma, basal cell carcinoma) and importance of early detection. It then reviews previous literature on skin cancer detection using methods like deep neural networks and support vector machines. The paper proposes detecting cancer types (melanoma, squamous, basal cell) using two approaches refined on two skin condition datasets. It aims to identify skin diseases across domains for more accurate detection and severity prediction of skin cancer.
IRJET- Skin Disease Detection using Image Processing with Data Mining and Dee...IRJET Journal
This document presents a skin disease detection method using image processing, data mining, and deep learning techniques. The proposed system uses a mobile application where users can upload images of affected skin areas. The images then undergo preprocessing like filtering and segmentation. Features are extracted from the images using techniques like 2D wavelet transform and GLCM. These features are classified using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that CNN achieves higher overall accuracy compared to SVM, with accuracies of 99.1% for CNN vs 90.7% for SVM.
IRJET- Analysis of Skin Cancer using ABCD TechniqueIRJET Journal
This document describes a proposed method for analyzing skin cancer using the ABCD technique. It begins with an introduction to skin cancer and melanoma. The proposed method involves preprocessing the skin lesion image using filters to reduce noise, segmenting the lesion from the image, extracting features using the ABCD parameters of asymmetry, border, color, and diameter, and then identifying malignant melanoma based on the feature analysis. If melanoma is detected early using this technique, it could help reduce healthcare costs by lowering the need for biopsies. The method aims to accurately detect melanoma for early treatment when survival rates are highest.
IRJET- Skin Cancer Detection using Digital Image ProcessingIRJET Journal
This document describes research on developing a system for detecting skin cancer through digital image processing. The system uses dermoscopic images of skin lesions that are preprocessed to remove noise. Texture features are then extracted from the images using Gray Level Co-occurrence Matrices and Gabor filtering. These features are input into a support vector machine for classification of images into cancerous or non-cancerous categories. The researchers achieved an accuracy of 77% and discuss potential improvements and applications of the system to help dermatologists detect melanoma and other skin cancers at early stages.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
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IRJET -Malignancy Detection using Pattern Recognition and ANNSIRJET Journal
This document discusses using pattern recognition and artificial neural networks (ANNs) to detect malignancy, specifically melanoma skin cancer. It describes preprocessing dermoscopy images to remove noise, then implementing an ANN with 12 neurons in each layer to classify images as cancerous or non-cancerous based on 12 selected features. After training the ANN, it is tested on new data for decision making. The method provides efficient classification compared to alternative gradient descent approaches that may result in incorrect predictions. Publicly available skin cancer data is used to train and validate the ANN model.
InnoMela is a new medical device developed by Innovative Imaging Concepts to help dermatologists detect melanoma. Melanoma is a dangerous form of skin cancer that is increasing in cases and causes many deaths each year. InnoMela uses multispectral analysis beyond what the human eye can see to improve the accuracy of identifying suspicious lesions. This has the potential to reduce errors in diagnosis and improve early detection of melanoma when treatment is most effective.
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.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Skin cancer exposed - Background informationXplore Health
This document provides background information on skin cancer and melanoma. It discusses the state of research on melanoma, including epidemiology, causes, diagnosis, prognosis, treatment options and molecular pathways. Ethical, legal and social aspects related to prevention, screening, treatment and research are also reviewed. The document is intended to help educators prepare lessons using multimedia resources on a skin cancer module.
Skin Lesion Classification using Supervised Algorithm in Data Miningijtsrd
Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and skin lesions is crucial.J48 Algorithm and SVM SUPPORT VECTOR MACHINE based techniques to estimate effort. In this work proposed system of the project is using data mining techniques for collecting the datasets for skin cancer. So that system can overcome to diagnosing the disease quickly and accuracy. Comparing to other algorithm proposed algorithm has more accuracy. When we have to using two kind of algorithm .They are J48, SVM. J48 Algorithm produced better accuracy more than SVM algorithm. The accuracy of the proposed system is 90.2381 . It means this prediction is very close to the actual values. G. Saranya | Dr. S. M. Uma "Skin Lesion Classification using Supervised Algorithm in Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29346.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29346/skin-lesion-classification-using-supervised-algorithm-in-data-mining/g-saranya
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
Optical properties of human skin as nonmelanoma skin cancer diagnosticsmathga
The document discusses using optical properties like absorption and scattering coefficients to detect nonmelanoma skin cancers like basal cell carcinoma and squamous cell carcinoma. It analyzes ex vivo data on the optical properties of normal and cancerous skin and finds the absorption and scattering coefficients can help distinguish cancer types. While results are promising, more data is needed accounting for factors like age, gender, and skin color to improve cancer classification.
Mymmo is a digital mammogram analysis software developed by Travancore Analytics. It is a Computer Aided Detection (CAD) system that assists the diagnostician or radiologist in early detection of breast cancer.
Highly experienced engineers in the fields of medical imaging, 3D and 2D image processing and various databases have developed this highly unique and efficient diagnostic tool.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Segmentation and Classification of Skin Lesions Based on Texture FeaturesIJERA Editor
Skin cancer is the most common type of cancer and represents 50% all new cancers detected each year. The deadliest form of skin cancer is melanoma and its incidence has been rising at a rate of 3% per year. Due to the costs for dermatologists to monitor every patient, there is a need for an computerized system to evaluate a patient‘s risk of melanoma using images of their skin lesions captured using a standard digital camera. In Proposed method, a novel texture-based skin lesion segmentation algorithm is used and to classify the stages of skin cancer using probabilistic neural network. Probabilistic neural network will give better performance in this system to detect a lot of stages in skin lesion. To extract the characteristics from various skin lesions and its united features gives better classification with new approached probabilistic neural network. There are five different skin lesions commonly grouped as Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Melanocytic Nevus / Mole (ML), Squamous Cell Carcinoma (SCC), Seborrhoeic Keratosis (SK). The system will be used to classify the queried images automatically to decide the stages of abnormality. The lesion diagnosis system involves two stages of process such as training and classification. Feature selection is used in the classified framework that chooses the most relevant feature subsets at each node of the hierarchy. An automatic classifier will be used for classification based on learning with some training samples of each stage. The accuracy of the proposed neural scheme is higher in discriminating cancer and pre-malignant lesions from benign skin lesions, and it attains an total classification accuracy is high of skin lesions.
Skin cure an innovative smart phone based application to assist in melanoma e...sipij
This document proposes a smart phone application called SKINcure that aims to assist with melanoma early detection and prevention. The application has two main components: 1) a UV alert module that notifies users of sunburn risk and calculates time to burn, and 2) an image analysis module that allows users to take skin images and classifies them as normal, atypical, or melanoma with 96.3-97.5% accuracy by analyzing features like hair detection, lesion segmentation, and classification algorithms. The proposed system utilizes a dermoscopy image database containing 200 images for development and testing, achieving high accuracy in detecting different lesion types automatically.
SKIN CANCER DETECTION AND SEVERITY PREDICTION USING DEEP LEARNINGIRJET Journal
This document discusses using deep learning techniques to detect skin cancer and predict its severity. It first describes the types of skin cancer (melanoma, squamous cell carcinoma, basal cell carcinoma) and importance of early detection. It then reviews previous literature on skin cancer detection using methods like deep neural networks and support vector machines. The paper proposes detecting cancer types (melanoma, squamous, basal cell) using two approaches refined on two skin condition datasets. It aims to identify skin diseases across domains for more accurate detection and severity prediction of skin cancer.
A State-of-the-art Review on Dielectric fluid in Electric Discharge Machining...IRJET Journal
This document proposes a non-invasive skin lesion analysis system for early detection of malignant melanoma using image processing in MATLAB. The system has two main parts: 1) a sunburn monitoring app to track sun exposure and 2) an automatic image analysis module. The image analysis module segments skin lesions, extracts features like shape, color and texture, and classifies lesions as benign, atypical or skin cancer with over 95% accuracy. It was tested on 200 dermoscopy images from a Portuguese hospital and achieved high classification performance. The proposed system provides an affordable, effective tool for early melanoma detection using a mobile phone platform.
Non-Invasive ABCD Monitoring of Malignant Melanoma Using Image Processing in ...IRJET Journal
This document proposes a non-invasive skin lesion analysis system for early detection of malignant melanoma using image processing in MATLAB. The system has two main parts: 1) a sunburn monitoring app to track sun exposure and 2) an automatic image analysis module. The image analysis module uses dermoscopy images from a hospital database to test segmentation, feature extraction, and classification algorithms. Hair is detected and excluded from images before segmentation. Features like shape, color and texture are extracted and classified using algorithms like k-NN achieving over 95% accuracy for benign, atypical and cancerous lesions. The system aims to provide an affordable, early screening tool for skin cancer detection on mobile devices.
Skin Cancer Detection Using Deep Learning TechniquesIRJET Journal
This document proposes a method to detect skin cancer using deep learning techniques. The method uses a dataset of 3000 skin cancer images to train models like YOLOR and EfficientNet B0. It involves pre-processing images by resizing, removing hair, and augmenting data. Features are extracted using YOLOR and images are classified into 9 classes of skin conditions using a CNN with EfficientNet B0 architecture. The models are trained and tested on the dataset, with results and discussion to follow in the next section.
Melanoma is a particularly dangerous type of skin cancer and is hard to treat in its later stages. Therefore, early detection is key in reducing mortality rates. In order to assist dermatologists in doing this, computer-aided systems have been designed for desktop computers. However, there is a desire for the development of mobile, at-home diagnostics for melanoma risk assessment. Here, we introduce a smartphone application that captures images and extracts ABCD features to classify skin lesions as either malignant or benign. The algorithms used are adaptive to make the process light and user-friendly, as well as reliable in diagnosis. Images can be taken with the phone's camera or imported from public datasets. The entire process of taking the image, performing preprocessing, segmentation and classification is completed on an Android smartphone in a short time. Our application is evaluated on a dataset of 200 images, and achieved either comparable or better performance metrics than other methods. Additionally, it is easy-to-download and easy-to-navigate for the user, which is important for the widespread use of such diagnostics.
Kalwa, U.; Legner, C.; Kong, T.; Pandey, S. Skin Cancer Diagnostics with an All-Inclusive Smartphone Application. Symmetry 2019, 11, 790. https://doi.org/10.3390/sym11060790
https://www.mdpi.com/2073-8994/11/6/790
Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.
Common Skin Disease Diagnosis and Prediction: A ReviewIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to diagnose common skin diseases. It begins with an abstract describing how algorithms like convolutional neural networks have shown promise in improving early detection of high-risk skin disorders. The document then reviews literature on using methods like CNNs, SVMs and Keras to classify skin diseases from images with over 90% accuracy. It also describes using techniques like data preprocessing, model building with deep neural networks, and backpropagation. Finally, it discusses challenges in dermatology diagnosis given variations in skin appearance and the potential of computer vision and deep learning models to provide an automated solution for identifying diseases from images.
The document discusses melanoma skin cancer detection using a computer-aided diagnosis system based on dermoscopic images. It begins with an introduction to skin cancer and melanoma. It then reviews existing literature on automated melanoma detection systems that use techniques like image preprocessing, segmentation, feature extraction and classification. Features extracted in other studies include asymmetry, border irregularity, color, diameter and texture-based features. The proposed system collects dermoscopic images and performs preprocessing, segmentation, extracts 9 features based on the ABCD rule, and classifies images using a neural network classifier to detect melanoma. It aims to develop an automated diagnosis system to eliminate invasive biopsy procedures.
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.
In this paper, we have tried to evaluate the chance of deep learning algorithm namely Convolutional Neural Network (CNN) to detect skin cancer classifying benign and malignant mole.
Melanoma Skin Cancer Detection using Deep LearningIRJET Journal
This document presents research on developing a deep learning model to detect melanoma skin cancer. The researchers created a convolutional neural network called Xception to analyze images of skin lesions and classify them as benign or malignant. They developed a web application using Flask that allows users to upload images for analysis. The Xception model achieved 97% accuracy on a test dataset. The web app was also able to accurately classify images, demonstrating its potential to assist dermatologists in early detection of melanoma skin cancer. However, further improvements are still needed before the model and web app can be fully relied upon for clinical diagnosis.
PSO-SVM hybrid system for melanoma detection from histo-pathological imagesIJECEIAES
This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.
A deep convolutional structure-based approach for accurate recognition of ski...IJECEIAES
One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework.
This document provides an overview of a capstone project to develop a machine learning model using convolutional neural networks to detect melanoma in mole images. The goal is to analyze photos taken with a smartphone to aid in early detection of skin cancer. The project will use a dataset of 2,000 labeled images to retrain the Inception-v3 model and evaluate its ability to correctly classify moles as benign or malignant. Key metrics like recall will be used to assess the model's performance, with the aim of achieving above human-level diagnostic accuracy. Data exploration found some inconsistencies that need addressing, such as stickers only present in benign images, to avoid biasing the model.
IRJET- Cancer Detection Techniques - A ReviewIRJET Journal
This document discusses techniques for detecting skin cancer through image processing. It begins with an introduction to cancer and skin cancer, noting that skin cancer is the most common type. It then outlines the main steps in skin cancer detection using images: pre-processing, segmentation, feature extraction, and classification. The document reviews several existing techniques for skin cancer diagnosis, including expert systems, frameworks using gray level co-occurrence matrix for feature extraction, and identifying skin diseases through various image processing techniques like segmentation and feature extraction. It also mentions magnetic resonance imaging can be used for diagnosis but has limitations in resolution and number of images.
In recent days, skin cancer is seen as one of the most Hazardous form of the Cancers found in
Humans. Skin cancer is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among
which Melanoma is the most unpredictable. The detection of Melanoma cancer in early stage can be helpful to
cure it. Computer vision can play important role in Medical Image Diagnosis and it has been proved by many
existing systems. In this paper, we present a survey on different steps which are being to detect the Melanoma
Skin Cancer using Image Processing tools. In every step, what are the different methods are be included in our
paper.
Skin disease detection and classification using different segmentation and cl...IRJET Journal
This document presents a study on using different image segmentation and classification techniques to detect and classify skin diseases. It explores region-based segmentation, thresholding, boundary detection, and entropy-based segmentation techniques. It also uses machine learning classifiers like support vector machines, decision trees, and random forests to classify diseases. The document evaluates these techniques on the HAM10000 public skin lesion dataset, which contains images of lesions from seven classes of diseases. The goal is to improve diagnostic accuracy by applying image processing and machine learning methods.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
Similar to Automated Screening System for Acute Skin Cancer Detection Using Neural Network and Texture (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.