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.
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.
An Innovative Approach for Automated Skin Disease Identification through Adva...IRJET Journal
This document describes a research project that aims to develop an automated skin disease identification system using advanced machine learning techniques like convolutional neural networks (CNNs). The proposed approach involves collecting a dataset of dermatological images, preprocessing the data, training a CNN model, and evaluating its performance in classifying different skin diseases. CNNs are able to accurately extract features from images and generalize to new data. The researchers achieve high accuracy, precision, and F1 scores, demonstrating the model's potential to assist dermatologists and enhance healthcare accessibility. The next steps involve developing a user-friendly web application to deploy the trained model for clinical use. The goal is to expedite skin disease diagnosis and improve patient outcomes through early detection and treatment.
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.
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
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.
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
Detection of Skin Cancer Based on Skin Lesion Images UsingDeep LearningIRJET Journal
This document discusses skin cancer detection using deep learning techniques. It begins with an introduction to skin cancer and the need for early detection. It then reviews the existing methods for skin cancer detection which rely on visual examination by dermatologists. The proposed method uses a deep learning model trained on skin lesion images to classify lesions as benign or malignant. The methodology section describes the image acquisition, preprocessing including enhancement, data augmentation, and preparation steps. It then discusses training a convolutional neural network for classification. Experimental results show the system can accurately detect different types of skin cancers like basal cell carcinoma and keratosis. The conclusion discusses benefits of developing such a system for integrated use on smartphones to enable low-cost cancer screening.
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.
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.
An Innovative Approach for Automated Skin Disease Identification through Adva...IRJET Journal
This document describes a research project that aims to develop an automated skin disease identification system using advanced machine learning techniques like convolutional neural networks (CNNs). The proposed approach involves collecting a dataset of dermatological images, preprocessing the data, training a CNN model, and evaluating its performance in classifying different skin diseases. CNNs are able to accurately extract features from images and generalize to new data. The researchers achieve high accuracy, precision, and F1 scores, demonstrating the model's potential to assist dermatologists and enhance healthcare accessibility. The next steps involve developing a user-friendly web application to deploy the trained model for clinical use. The goal is to expedite skin disease diagnosis and improve patient outcomes through early detection and treatment.
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.
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
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.
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
Detection of Skin Cancer Based on Skin Lesion Images UsingDeep LearningIRJET Journal
This document discusses skin cancer detection using deep learning techniques. It begins with an introduction to skin cancer and the need for early detection. It then reviews the existing methods for skin cancer detection which rely on visual examination by dermatologists. The proposed method uses a deep learning model trained on skin lesion images to classify lesions as benign or malignant. The methodology section describes the image acquisition, preprocessing including enhancement, data augmentation, and preparation steps. It then discusses training a convolutional neural network for classification. Experimental results show the system can accurately detect different types of skin cancers like basal cell carcinoma and keratosis. The conclusion discusses benefits of developing such a system for integrated use on smartphones to enable low-cost cancer screening.
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.
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.
Detection of Skin Diseases based on Skin lesion imagesIRJET Journal
This document discusses a study on detecting skin diseases from skin lesion images using deep learning algorithms. The researchers collected a dataset of skin images from the International Skin Imaging Collaboration repository. They used two deep neural networks - a convolutional neural network (CNN) and ResNet50 - to classify the images as malignant, benign, or normal. The CNN and ResNet50 models were trained on 80% of the dataset and tested on the remaining 20%. The results showed that the deep learning algorithms could accurately identify different types of skin cancers and diseases from images, which could help in the early detection of skin cancer.
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.
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
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
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.
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.
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.
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.
A convolutional neural network for skin cancer classificationIJICTJOURNAL
Skin diseases can be seen clearly by oneself and others. Although this disease is visible on the skin, we fear that this skin disease is harmful. People who experience skin diseases immediately visit a dermatologist to have their complaints and symptoms checked. This skin protects the body, especially from the sun, so it can be lethal if something goes wrong. One example of deadly skin disease is skin cancer or skin tumors. In this research, we classified skin cancer into Benign and Malignant using the convolution neural network (CNN) algorithm. The purpose of this research is to develop the CNN architecture to help identify skin diseases. We used a dataset of 3,297 skin cancer images which are publicly available on the Kaggle website. We propose two CNN architectures that differ in the number of parameters. The first architecture has 6,427,745 parameters, and the second architecture has 2,797,665. The accuracy of the proposed models is 93% and 74% respectively. The first model with the number of parameters 6,427,745 was saved for use in the creation of the website. We created a web-based application with the Django framework for skin disease identification.
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 - Classification of Cancer Images using Deep LearningIRJET Journal
This document presents a methodology for classifying breast cancer histopathology images using deep learning. Specifically, it aims to classify images as either invasive ductal carcinoma (IDC) or non-IDC using a convolutional neural network (CNN) model. The proposed methodology involves preprocessing the images, building a CNN with convolutional, pooling and fully connected layers, training the model on labeled image data, and using the trained model to classify new images as IDC or non-IDC. The goal is to develop an automated system for early and accurate detection of breast cancer subtypes to improve diagnosis and patient outcomes.
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%.
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.
Comparing the performance of linear regression versus deep learning on detect...journalBEEI
This document compares the performance of linear regression versus deep learning models for detecting melanoma skin cancer using images. Two machine learning models were developed - one using linear regression for image classification and one using a convolutional neural network (CNN) for object detection. Both models were trained on 600 skin images from a public database and tested on 120 separate images. The testing results showed that the CNN model achieved 70% accuracy compared to 68% for the linear regression model. More importantly, the linear regression model had a 43% false-negative rate, much higher than the CNN's 25% rate. A high false-negative rate could result in delayed treatment and worse health outcomes. Therefore, the document concludes that the CNN model is the best approach for detecting
Automated Intracranial Neoplasm Detection Using Convolutional Neural NetworksIRJET Journal
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
IRJET - Detection of Skin Cancer using Convolutional Neural NetworkIRJET Journal
This document presents a method for detecting skin cancer using convolutional neural networks. The proposed method involves collecting skin images, preprocessing them by removing noise and segmenting regions of interest, extracting features like asymmetry, border, color, and diameter, performing dimensionality reduction using principal component analysis, calculating dermoscopy scores, and classifying images as malignant or benign using a convolutional neural network (CNN) model. The CNN model achieves 92.5% accuracy in classification. The document provides background on skin cancer and challenges with traditional biopsy methods. It describes the system architecture including data collection, preprocessing, segmentation, feature extraction, and classification steps. Key aspects of CNNs like convolutional, ReLU, pooling, and fully connected layers are also overviewed
This document provides a summary of different methods for skin disease classification from images. It begins with an introduction describing skin diseases and the need for automated classification systems. It then discusses traditional/handcrafted feature-based classification approaches which involve preprocessing, segmentation, feature extraction and classification using models like SVM. Deep learning-based approaches using CNNs are also covered. The document surveys related works on both traditional and deep learning methods, analyzing their performance on various skin disease datasets and image types. Traditional methods achieved up to 98% accuracy while deep learning methods have shown human-level performance in some cases. The document concludes with an analysis of traditional versus CNN-based methods.
Intracranial-Tumor Detection and Classification System using Convnet and Tran...IRJET Journal
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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
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3-6 June 2024, Niš, Serbia
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.