DeepSkinNet: A Deep Learning Induced Skin Lesion Extraction System from Dermoscopic Images

Abstract

In this work, a DeepSkinNet model was developed based on an encoder-decoder type framework. The designed encoder incorporates three blocks where each block sandwiches convolution, rectified linear unit (ReLU), and maxpooling layers to retain the prominent details. The developed DIL (dilated convolution + instance normalization + Leaky ReLU) module comprises three branches, where each branch consists of an atrous convolution layer with a sampling rate of two, followed by instance normalization and a Leaky ReLU activation function to retain the subtle details accurately. Further, the proposed decoder network with a feature fusion mechanism stacks convolution, transposed convolution, and ReLU activation functions to precisely extract the lesion regions from the dermoscopic images. The efficacy of the designed DeepSkinNet is validated through subjective as well as objective analysis and found to be suitable for medical diagnosis against various SOTA methods. The Dice coefficients (DC) found using ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets are found to be 93.33%, 89.00%, 92.05%, and 91.24%, respectively.