MELANOMA DETECTION USING DEEP LEARNING
Melanoma is considered as one of the fatal cancer in the world, this form of skin cancer may spread to other parts of the body in case that it has not been diagnosed in an early stage. Thus, the medical field has known a great evolution with the use of automated diagnosis systems that can help doctors and even normal people to determine a certain kind of disease. In this matter, we introduce a method for melanoma skin cancer detection that can be used to examine any suspicious skin. Our proposed system rely on the prediction of different methods: A convolutional neural network and trained with a set of features describing the borders, texture and the color of a skin lesion. This method is then combined to improve their performances using RESNET15v2. The experiments have shown that using the method together, gives the highest accuracy level. Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.
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