Skin Lesion Segmentation with Improved Convolutional Neural Network
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ORIGINAL PAPER
Skin Lesion Segmentation with Improved Convolutional Neural Network Şaban Öztürk 1 & Umut Özkaya 2
# Society for Imaging Informatics in Medicine 2020
Abstract Recently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used for the segmentation of full-resolution skin lesion images without any pre- or post-processing. It is to support the residual structure of the FCN architecture with spatial information. This situation, which creates a more advanced residual system, enables more precise detection of details on the edges of the lesion, and an analysis independent of skin color can be performed. It offers two contributions: determining the center of the lesion and clarifying the edge details despite the undesirable effects. Two publicly available datasets, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets, are used to evaluate the performance of the iFCN method. The mean Jaccard index is 78.34%, the mean Dice score is 88.64%, and the mean accuracy value is 95.30% for the proposed method for the ISBI 2017 test dataset. Furthermore, the mean Jaccard index is 87.1%, the mean Dice score is 93.02%, and the mean accuracy value is 96.92% for the proposed method for the PH2 test dataset. Keywords Skin lesion segmentation . CNN . FCN . Segmentation . Melanoma
Introduction Melanoma is one of the most common types of cancer, resulting from the uncontrolled distribution of the skin cell called melanocytes [1]. According to the annual report of the American Cancer Society in the USA, 96,480 cases are diagnosed as new melanoma cases, and the estimated mortality rate is 7230 [2]. Compared with the report in 2017, the number of cases increased by 9370 [3]. Since melanoma is known to be the most lethal skin cancer, this increase is hazardous for human life. Despite this adverse situation, early detection and treatment significantly increase the chances of survival [4]. As a conventional method, dermatologists perform a visual * Şaban Öztürk [email protected] 1
Technology Faculty, Electrical and Electronics Engineering, Amasya University, Amasya, Turkey
2
Engineering and Natural Science Faculty, Electrical and Electronics Engineering, Konya Technical University, Konya, Turkey
examination for the detection of melanoma. This method is time-consuming, requires a well-trained expert, and suffers from inter-observer variation [5]. Besides, visual similarities between the lesions, variations in lesion shapes, and dis
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