Technology Faculty, Electrical and Electronics Engineering, Amasya University, Amasya, Turkey.
Engineering and Natural Science Faculty, Electrical and Electronics Engineering, Konya Technical University, Konya, Turkey.
J Digit Imaging. 2020 Aug;33(4):958-970. doi: 10.1007/s10278-020-00343-z.
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.
近年来，皮肤癌的发病率显著增加，严重威胁着人类的健康。这种疾病早期发现至关重要，但自动检测却极具挑战性。皮肤病变图像存在不理想的残留物（毛发、标尺标记）、不清晰的边界、可变的对比度、形状差异和颜色差异等因素，使得自动分析变得非常困难。为了克服这些挑战，本文提出了一种基于全卷积网络（FCN）的高效分割方法。所提出的改进 FCN（iFCN）架构用于对未经任何预处理或后处理的全分辨率皮肤病变图像进行分割。它支持 FCN 架构的残差结构，使用空间信息。这种情况创建了一个更先进的残差系统，能够更精确地检测病变边缘的细节，并且可以进行独立于肤色的分析。它有两个贡献：确定病变的中心，并在存在不理想影响的情况下澄清边缘细节。两个公开可用的数据集，即 IEEE 国际生物医学成像研讨会（ISBI）2017 挑战赛和 PH2 数据集，用于评估 iFCN 方法的性能。对于 ISBI 2017 测试数据集，所提出方法的平均 Jaccard 指数为 78.34%，平均 Dice 分数为 88.64%，平均准确率为 95.30%。此外，对于 PH2 测试数据集，所提出方法的平均 Jaccard 指数为 87.1%，平均 Dice 分数为 93.02%，平均准确率为 96.92%。
