Skin Lesions Segmentation and Classification for Medical Diagnosis

Abstract

Classification and segmentation of various skin lesions play a very important role in the field of dermoscopy. Using computer-aided applications to detect cancerous cells and predict the lesion as benign and malignant can yield better results. Automatic estimation of skin disease from skin lesion images help practitioners to perform rapid diagnosis, provide early treatment and quick decision making. In this paper, Convolution Neural Network (CNN) is used to identify cancer prone skin lesions from dermoscopy images. Experiments were performed on ISIC 2016 data set with two lesion classes (Malignant and Benign). The training was carried out with the Multiple Residual Neural Network (ResNet) architecture, where the data is pre-processed with different methods. Finally, the comparative analysis with other methods was also performed. The results indicated that the performance of our proposed method is also in line with state of the art methods.