Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture

The rise of deep learning techniques, such as a convolutional neural network (CNN) in solving medical image problems, offered fascinating results that motivated researchers to design automatic diagnostic systems. Image segmentation is one of the crucial and challenging steps in the design of a computer-aided diagnosis system owing to the presence of low contrast between skin lesion and background, noise artifacts, color variations, and irregular lesion boundaries. In this paper, we propose a modified and improved encoder-decoder architecture with a smaller network depth and a smaller number of kernels to enhance the segmentation process. The network performs segmentation for skin cancer images to obtain information about the infected area. The proposed model utilizes the power of the VGG19 network’s weight layers for calculating rich features. The deconvolutional layers were designed to regain spatial information of the image. In addition to this, optimized training parameters were adopted to further improve the network’s performance. The designed network was evaluated for two publicly available benchmarked datasets ISIC, and PH2 consists of dermoscopic skin cancer images. The experimental observations proved that the proposed network achieved the higher average values of segmentation accuracy 95.67%, IoU 96.70%, and BF-score of 89.20% on ISIC 2017 and accuracy 98.50%, IoU 93.25%, and BF-score 84.08% on PH2 datasets as compared to other state-of-the-art algorithms on the same datasets.