Skin Cancer Classification from Dermoscopic Images using Feature Extraction Methods

Abstract

Melanoma is a type of skin cancer that is mainly caused by intense UV exposure. If melanoma is identified at an early stage, then it is generally remediable. However, if it is not diagnosed properly, cancer can grow to rest of the body which then makes it difficult to cure and can be lethal. Conventionally, melanoma is diagnosed through visual methods and biopsies but their accuracy may not be reliable for all the cases. Hence, the risks involved for such a diagnosis have emerged identification and classification of melanoma as benign or malignant a very important research problem in medical imaging. This paper employs various feature descriptors like local binary pattern (LBP), complete LBP (CLBP) and their variants, which are based on histogram mapping such as uniform, rotation invariant and rotation invariant uniform patterns. The extracted features are then used to train different classifiers such as decision tree, random forest (RF), support vector machine (SVM) and k nearest neighbour (kNN). A comparative study of the various feature descriptors and classifiers are analyzed for accurate identification and classification of melanoma as benign or malignant. An image dataset which has been used in our work has been downloaded from ISIC-Archive, which consists of 947 dermoscopic images and the dataset is made freely available online by realizing the importance of the research. The best accuracy has been obtained by using RF in CLBP with an accuracy of 80.3%.