Deep Learning Model for Skin Lesion Segmentation: Fully Convolutional Network

Abstract

Segmentation of skin lesions is a crucial task in detecting and diagnosing melanoma cancer. Incidence of melanoma skin cancer which is the most deadly form of skin cancer has been on steady increase. Early detection of the melanoma cancer is necessary to improve the survival rate of the patients. Segmentation is an important task in analysing skin lesion images. Skin lesion segmentation has come with some challenges such as low contrast and fine grained nature of skin lesions. This has necessitated the need for automated analysis and segmentation of skin lesions using state-of-the-arts techniques. In this paper, a deep learning model has been adapted for the segmentation of skin lesions. This work demonstrates the segmentation of skin lesions using fully convolutional networks (FCNs) that train skin lesion images from end-to-end using only the images pixels and disease ground truth labels as inputs. The fully convolutional network adapted is based on U-Net architecture. The model is enhanced by employing multi-stage segmentation approach with batch normalisation and data augmentation. Performance metrics such as dice coefficient, accuracy, sensitivity and specificity were used for evaluating the performance of the model. Experimental results show that the proposed model achieved better performance compared with the other state-of-the arts methods for skin lesion image segmentation with a dice coefficient of $90\%$ and sensitivity of $96\%$.