Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN

Fully Convolution Networks have recently become popular for tackling semantic segmentation problems. However, its performance is dependent on the hyper-parameters it selects, and manually fine-tuning these hyper parameters is a time-consuming task. Hence, in this paper, a hyper-parameter optimized Fully Convolution Encoder Decoder Network (FCEDN) is proposed for dermoscopy image segmentation. The hyper-parameters of the network are optimized by a novel Exponential Neighborhood Grey Wolf Optimization (EN-GWO) algorithm. In EN-GWO, a neighborhood based searching strategy is defined by blending the wolves' individual haunting strategies with their global haunting strategies, emphasizing the right balance between exploration and exploitation. A comprehensive study is conducted using the International Skin Imaging Collaboration (ISIC) 2016 and ISIC 2017 datasets to validate the EN-GWO compared with four variants of GWO, GA, and PSO based hyper-parameter optimization techniques. For the ISIC 2016 and ISIC 2017 datasets, the proposed model can segment skin cancer images with a Jaccard coefficient of 96.41%, 86.85%, Dice coefficient of 98.48%, 87.23%, and accuracy of 98.32%, 95.25% respectively. It is evident from the experimental results that the proposed model outperforms other recent deep learning models such as U-Net, Link-Net, SegNet, and FCN.