Skin lesion segmentation using fully convolutional networks: A comparative experimental study

Because the most dangerous type of skin cancer, melanoma, is very difficult for dermatologists to detect because of the low contrast between the lesion and the adjacent skin, the automatic application of skin lesion segmentation is regarded as very challenging. This paper proposes the implementation of a medical image segmentation that will accelerate a melanoma diagnosis by dermatologists. In the implementation, Fully Convolutional Network (FCN) architectures generated by modifying Convolutional Neural Network (CNN) architectures are used. The proposed algorithm for an automatic semantic segmentation of skin lesions utilizes four different FCN architectures, FCN-AlexNet, FCN-8s, FCN-16s, and FCN-32s. The experimental studies in this paper are constructed on the ISIC 2017 dataset, and the evaluations of these architectures on the dataset are carried out for the first time with this study. In the experimental studies, once the images in the dataset are preprocessed, the FCNs are first trained separately. Secondly, the accuracies and Dice coefficients on the validation dataset are calculated by using these trained FCN architectures. Thirdly, the obtained results are compared. Finally, the inferences of lesion segmentation are visualized in order to exhibit how exactly the FCN architectures can segment the lesions. The experimental results show that the FCNs in the proposed algorithm are suitable for skin lesion segmentation. In addition, it is thought that the experimental results will contribute to the scientific literature and assist the researchers who are working on medical image segmentation. (c) 2020 Elsevier Ltd. All rights reserved.