UCM-NetV2:一种高效、准确的皮肤病变分割深度学习模型

Chunyu Yuan , Dongfang Zhao , Sos S. Agaian
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引用次数: 0

摘要

从皮肤镜图像中准确分割皮肤病变对于早期皮肤癌检测至关重要,然而病变外观和图像伪影的变化带来了挑战。本研究提出了一种高效的深度学习模型UCM-NetV2,以提高准确性和计算效率。UCM-NetV2通过结合多层感知器和CNN层的新颖“网络结构”增强了UCM-Net架构,提高了预测精度,同时保持了只有0.046万个参数的超轻量级设计。对ISIC2017和ISIC2018数据集的评估表明,UCM-NetV2在精度和计算效率方面优于现有方法,实现的推理速度比U-Net快67倍,所需的推理速度低于0.04 GFLOPs。这些进步使皮肤病变分析更容易获得,特别是在资源有限的环境中,使主动皮肤健康监测和促进远程皮肤病学成为可能。为了促进移动医疗诊断的进一步创新,UCM-NetV2的源代码位于https://github.com/chunyuyuan/UCMV2-Net。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UCM-NetV2: An efficient and accurate deep learning model for skin lesion segmentation
Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes an efficient deep learning model, UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel "cyber-structure" com- bining Multilayer Perceptron and CNN layers, improving prediction accuracy while maintaining an ultra-lightweight design with only 0.046 million parameters. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in accuracy and com- putational efficiency, achieving up to 67 times faster inference speeds than U-Net and requiring less than 0.04 GFLOPs. These advancements make skin lesion analysis more accessible, particularly in resource-limited settings, enabling proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 is on https://github.com/chunyuyuan/UCMV2-Net.
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