Melanoma skin cancer analysis using convolutional neural networks-based deep learning classification

Abstract

Melanoma, a variant of a world-threatening disease known as skin cancer, is less common but the most serious type which develops in the cells that produce melanin (gives pigmentation to the human skin). Melanoma risk appears to be rising among those under 40, particularly women. The major signs of melanoma include a variation in a normal-sized mole and the appearance of unusual pigment in the skin. Hence the detection of melanoma should be narrowed down to moles that are different in size possessing a diameter larger than 6 mm and color combinations that are not normal. Melanoma is more susceptible to spreading to many other regions of the body if it is not detected and treated early. A traditional medical way for detection of melanoma is epiluminescence microscopy or dermoscopy but unfortunately that was not much easier way as melanoma was considered a master of obscure, as in recent years the field of medicine was into great evolution scientists had invented automated diagnoses to detect these kinds of life dire disease at its very earlier stage. The dataset taken was from the skin cancer pictures from the ISIC archive. This research works on an approach for predicting skin cancer by classifying images as either malignant or benign using deep convolutional neural networks. Algorithms such as Inception v3 and MobileNetV2 were finalized among other deep learning approaches as the accuracy it provided was 99.13% and 92.46%, respectively, and it had given a minimal loss. Assessing research articles published in reputable journals on the topic of skin cancer diagnosis. For easier understanding, research findings of all the algorithms are presented in graphs, comparison tables, techniques, and frameworks. The unique stand of the chapter is that a web application for which the skin lesion or skin texture abnormality image is given as an input and predicts if the skin is either malignant or benign very accurately.