This research aims to improve the efficiency of skin lesion segment locations for the given input image of skin cancer using a combination of recently modified segmentation algorithms. Skin lesion segmentation is still a challenging task in medical image analysis because of the low contrast and high noise produced by dermoscopic imaging. Previous works extracted spatially‐oriented information but failed in terms of training. They were based on convolutional neural networks (CNNs), which require extensive training time. Current results show 91% to 93% efficiency in segmentation, but the proposed segmentation capsule network (SegCaps) in this research has improved it up to 98% by adding four pre‐processing sequential processes in combinations with SegCaps algorithms. The performance of the proposed SegCaps model was evaluated on two different datasets—ISBI 2017 and PH2 and implemented on the MatlabR2017b software. The chosen metrics were Jaccard co‐efficient, dice similarity co‐efficient, accuracy, sensitivity, and specificity for validation.

中文翻译：

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SegCaps：皮肤镜图像中基于SegCaps网络的高效皮肤病变分割

这项研究旨在结合最近修改的分割算法，针对给定的皮肤癌输入图像，提高皮肤病变分割位置的效率。由于皮肤镜成像产生的低对比度和高噪声，皮肤病变分割在医学图像分析中仍然是一项艰巨的任务。先前的作品提取了面向空间的信息，但在训练方面却失败了。它们基于卷积神经网络（CNN），需要大量的训练时间。目前的结果表明，分割效率为91％到93％，但是本研究中提出的分割胶囊网络（SegCaps）通过与SegCaps算法结合添加四个预处理顺序过程，将分割效率提高到98％。建议的SegCaps模型的性能在两个不同的数据集（ISBI 2017和PH2）上进行了评估，并在MatlabR2017b软件上实现。选择的指标为Jaccard系数，骰子相似系数，准确性，敏感性和验证特异性。