Analysis of Melanoma Lesion Images using Feature Extraction & Classification Algorithms

Abstract

Among the most dangerous of cancers found in human beings, skin cancer is the prevalent one. These are of various forms. The most sporadic among them is melanoma. Early phase identification of melanoma will be helpful in curing it. Intensive skin exposure to UV radiation is the principal cause of melanoma. In this article, along with other techniques for extracting features (LDP [Local Directional Patterns], LBP [Local Binary Patterns], Convolutional Neural Networks [CNN]), we have used an SVM classifier for the study of melanoma skin photos. Such suggested algorithms are best graded when opposed to other recognition schemes. The LBP and LDP gives us means to extract features; these figures are subsequently used for identification of derived features from these methods or algorithms and classified or separated by the SVM (Support Vector Machine) classifier. For many of the classifications of melanoma skin images using these algorithms, we have accuracy nearly above 80 %, whereby the LBP system together with the SVM classifier was the most powerful attribute extraction tool of the three with their polynomial kernel type. Thus using this algorithm-classifier, the melanoma skin lesion images can be detected and diagnosed by the doctors in its early stage itself, resultantly, helping save lives.