基于EfficientViT的混合深度学习框架用于胃肠道疾病的分类
Vishesh Tanwar1, Bhisham Sharma2, Dhirendra Prasad Yadav3
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
Scientific reports
|July 24, 2025
概括
一个新的深度学习模型,高效视觉转换器 (EfficientViT),从内镜图像准确诊断胃肠道 (GI) 疾病. 这种人工智能工具显著改进了现有的早期和可靠的肠道疾病检测方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 胃肠道 (GI) 疾病是全球主要的健康问题,需要早期和准确的诊断.
- 目前的诊断方法如内镜是耗时的,并且严重依赖于医生的解释.
- 人工智能的进步为提高诊断准确性和效率提供了潜力.
研究的目的:
- 开发和评估一种新的深度学习模型,即高效视觉转换器 (EfficientViT),用于分类八种胃肠道疾病.
- 利用EfficientNetB0和Vision Transformer (ViT) 的优势,在肠道内镜图像中增强特征提取.
- 为临床医生提供更可靠,更准确的工具来诊断胃肠道疾病.
主要方法:
- 拟议的高效视觉变压器 (EfficientViT) 模型结合了EfficientNetB0和视觉变压器 (ViT).
- EfficientViT使用EfficientNetB0进行本地纹理和多尺度特征捕获,并使用ViT进行全球上下文识别.
- 双块设计通过同时处理本地细节 (EfficientNet) 和全球依赖 (编码器块) 的输入来优化模型.
主要成果:
- 在分类胃肠道疾病方面,EfficientViT实现了99.82%的卓越准确率.
- 与基于MobileNetV2的模型相比,该模型表现出更高的性能 (准确率为99.60%).
- EfficientViT表现出极好的精度,回忆和F1分数,表现优于现有方法.
结论:
- EfficientViT提供了一个高度准确和可靠的AI驱动工具,用于从内镜图像中诊断胃肠道疾病.
- 该模型能够捕捉本地和全球特征,从而增强诊断能力.
- 这种深度学习方法为胃肠病学临床实践带来了有前途的进步.
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