The segmentation of dermoscopic-based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11-layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning.

中文翻译：

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用于皮肤镜图像中皮肤病变分割的有效卷积深度神经网络的体系结构

由于各种因素，基于皮肤镜的皮肤病变图像的分割被认为具有挑战性。一些最明显的原因包括受影响的皮肤病变附近对比度差、病变范围模糊且不可预测、存在噪声变化以及在不同条件下捕获图像。本文旨在开发一种基于深度学习的针对不同皮肤病变的皮肤镜图像的高效分割模型。本文提出了 11 层卷积深度神经网络，具有从头到尾训练的两个分割模型，并且不依赖于有关数据的任何先前信息。模型的可行性、效率和推测能力在 ISIC2018 数据库上进行评估。所提出的模型在皮肤病变的分割中达到了 0.903 的准确度和 0.820 的 Jaccard 指数。