基于FNLM预处理的SCU-Net皮肤病灶分割

V. S. S. P. R. Gottumukkala, N. Kumaran, V. Sekhar
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引用次数: 0

摘要

最近,由于辐射问题、大气影响和环境条件的变化,人们患上了各种各样的皮肤癌。因此,早期发现皮肤癌可以挽救数百万人的生命。传统的图像处理方法不能准确定位疾病影响区域,导致皮肤癌类型预测不准确。为此,本文实现了基于预处理的皮肤病灶分割网络(SLS-Net)。首先,采用快速非局部均值(FNLM)滤波去除皮肤损伤中不同类型的噪声,增强皮肤损伤图像。进一步,采用基于跳跃连接的U-Net (SCU-Net)模型对皮肤病变进行精确分割。在ISIC-2019和PH2数据集上进行的模拟表明,与传统分割模型相比,所提出的SLS-Net在精度、召回率、灵敏度、特异性和f1评分方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skin Lesion Segmentation Using SCU-Net with FNLM Preprocessing
Recently, people are suffering with variety of skin cancers due to radiation problems, atmospheric effects and change in environmental conditions. So, early detection of skin cancers can save the millions of people. The conventional image processing methods were failed to localize disease effected region accurately, which caused improper prediction of skin cancer types. Therefore, this article is implemented the preprocessing-based skin lesion segmentation network (SLS-Net). Initially, fast nonlocal mean (FNLM) filter is applied to remove the different types of noises from skin lesions, which also enhances the skin lesion image. Further, skip connection-based U-Net (SCU-Net) model is applied for accurate segmentation of skin lesions. The simulations performed on ISIC-2019 and PH2 datasets discloses the superiority of proposed SLS-Net in terms of precision, recall, sensitivity, specificity, and f1-score as compared to conventional segmentation models.
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