Convolutional Neural Network For Multiclass Skin Cancer Image Classification

Skin cancer is the most dangerous, having one-third diagnosed rate in worldwide. It is supposed to spread vastly to other body parts when it is not detected at beginning stage. Skin cancer diagnosing system contains three major steps such as segmentation, feature extraction and classification. Firstly, images are pre-processed using 2D median filter to remove some artifacts like thin hair, gel, air bubble etc. followed by segmentation using discrete wavelet transform to extract lesion region from background skin region is described in our last paper. In this paper, we have passed previously segmented images to Convolutional Neural Network (CNN) for feature learning and classification. Experimentation conducted on three classes of PH2 dataset, classification results are satisfactory and accurate.