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提高皮肤癌分类:一个多层次的注意力和视觉转换器的合奏方法

Guang Yang¹, Suhuai Luo², Peter Greer²

¹School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia.

Sensors (Basel, Switzerland)

|April 26, 2025

概括

这项研究引入了一种基于注意力的新方法,以改进皮肤癌分类的深度学习模型. 改进的模型在ISIC2018数据集上实现了95.05%的准确性,有助于早期发现皮肤癌.

科学领域:

皮肤病学和医学成像学
医疗保健中的人工智能
计算机视觉计算机视觉

背景情况:

皮肤癌,特别是黑色素瘤,对全球健康构成重大风险,需要早期检测以改善患者的治疗结果.
深度学习模型在皮肤癌分类方面表现有前途,但难以准确区分良性和恶性病变.
目前的方法在精确识别皮肤病变图像中的关键诊断特征方面面临挑战.

研究的目的:

为皮肤癌分类中的深度学习模型开发和评估一种新的基于注意力的多尺度绩效助推器.
提高视觉变压器 (ViT) 和卷积神经网络 (CNN) 模型在区分良性和恶性皮肤病变的准确性.
通过使用注意力机制,改善对相关图像区域的诊断焦点.

主要方法:

引入了一种基于注意力的多尺度性能增强器,灵感来源于视觉转换器 (ViT) 架构.
利用注意力图来引导模型到皮肤损伤图像中的诊断相关区域.
使用多数投票的集体学习来结合来自多个深度学习模型 (ViT和EfficientNet) 的预测.

主要成果:

提出的方法显著提高了ViT和CNN模型的准确性.
组合分类器结合了ViT和EfficientNet,在ISIC2018数据集上实现了95.05%的分类准确度.
与单个深度学习模型相比,综合方法显示出更高的性能.

结论:

这种新的基于注意力的多尺度学习方法有效地提高了皮肤癌分类的准确性.
合并方法与注意力机制相结合,为改善医学成像诊断性能提供了强大的策略.
这些发现突显了先进的人工智能技术在早期和准确地检测皮肤癌的潜力.

关键词:

深度学习是一种深度学习.图像处理是图像处理的过程.神经网络的神经网络的神经网络皮肤癌的分类皮肤癌的分类变压器变压器变压器变压器

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