可解释深度学习用于增强胃肠内镜图像的多类分类
Astitva Kamble1, Kushagra Parashar2, Elbert Ronnie3
1Information Technology, Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, Gwalior, Madhya Pradesh, 474015, INDIA.
Biomedical physics & engineering express
|December 11, 2025
概括
这项研究增强了使用EfficientNetB3的胃肠道 (GI) 内镜图像分析,以提高检测疾病的准确性. 该模型达到94.25%的准确性,提供可解释的结果和一个用户友好的临床应用界面.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 胃肠道内镜对于诊断胃肠道疾病至关重要.
- 深度学习模型已经在医学图像中进行了先进的异常检测.
- 现有的方法往往严重依赖于数据增强.
研究的目的:
- 开发一种增强的深度学习方法,以提高肠道内镜图像的分类准确性.
- 创建一个保持中等复杂性的模型,而无需进行大量的数据增强.
- 提高人工智能驱动的诊断工具的可解释性和可访问性.
主要方法:
- 利用了Kvasir数据集,在八个类别中使用了8,000张标记的内镜图像.
- 采用了EfficientNetB3作为骨干架构.
- 综合局部可解释的模型不可知解释 (LIME) 解释性.
- 使用Gradio开发了一个用户友好的界面,用于实际应用.
主要成果:
- 达到94.25%的测试准确度.
- 获得的精度为94.29%和回忆率为94.24%.
- LIME突出地图有效地突出了影响预测的关键图像区域.
结论:
- 拟议的模型证明了GI内镜图像分析的高准确性和可解释性.
- 集成LIME和Gradio接口可以提高临床可用性.
- 这项工作突出了人工智能的潜力,以推进医学成像诊断.
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