基于视觉转换器 (ViT) 的膀癌的分类
Ola S Khedr1, Mohamed E Wahed2, Al-Sayed R Al-Attar3
1Department of Mathematics -Computer Science, Faculty of Science, Suez Canal University, Ismailia, 44745, Egypt. ola_salah@science.suez.edu.eg.
Scientific reports
|November 24, 2023
概括
深度学习模型准确地分类了膀癌的亚型. 视觉转换器 (ViT_B32,ViT_B16) 在非侵入性,侵入性和正常膀组织分类方面表现出高准确度.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 膀癌是一种常见的恶性瘤,具有不同的侵入性和非侵入性亚型.
- 准确的分类对于有效的治疗策略和患者预后至关重要.
研究的目的:
- 评估深度学习模型,将膀癌分为正常,非侵入性和侵入性类型.
- 为了比较各种卷积神经网络和视觉转换器在组织病理图像上的性能.
主要方法:
- 使用了膀组织组织病理学图像的数据集,分为培训 (70%),验证 (15%) 和测试 (15%) 集.
- 评估的深度学习架构是:EfficientNetB2,InceptionResNetV2,InceptionV3,ResNet50V2和视觉转换器 (ViT_B32,ViT_B16),这些架构都在进行深度学习.
主要成果:
- 视觉变压器ViT_B32获得了最高的精度 (99.49%),紧随其后的是ViT_B16 (99.23%).
- InceptionResNetV2也表现出高性能,准确度达到了98.73%.
- 其他模型,如EfficientNetB2 (95.43%) 和ResNet50V2 (93%),显示出具有竞争力的结果.
结论:
- 深度学习模型,特别是视觉转换器,对于分类膀癌亚型非常有效.
- 这些发现支持AI在改善瘤学诊断准确性和临床决策方面的潜力.
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