用混合网络自动识别10类胃肠道异常，提升内镜诊断效率
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images
- 融合ResNet与DenseNet的集成模型处理内镜图像
- 整体准确率达94%，对正常情况召回率高达98%
- 适合需要快速筛查胃肠道异常的临床医生使用
本文提出一种深度学习框架，用于视频胶囊内镜（VCE）图像中胃肠道异常的多类别分类，旨在自动化识别包括血管扩张、出血、溃疡在内的十类异常，减轻胃肠科医生的诊断负担。采用DenseNet与ResNet架构的集成模型，在结构良好的数据集上达到94%的整体准确率。精确率从红斑的0.56到虫体的1.00不等，正常发现的召回率最高达98%。本研究强调了归一化与增强等稳健预处理技术对模型性能的提升作用。工作贡献在于开发了一款高效的AI辅助工具，优化胃肠科诊断流程，最终改善患者诊疗结果。
原文摘要 · Abstract (English)
This paper presents a deep learning framework for the multi-class classification of gastrointestinal abnormalities in Video Capsule Endoscopy (VCE) frames. The aim is to automate the identification of ten GI abnormality classes, including angioectasia, bleeding, and ulcers, thereby reducing the diagnostic burden on gastroenterologists. Utilizing an ensemble of DenseNet and ResNet architectures, the proposed model achieves an overall accuracy of 94\% across a well-structured dataset. Precision scores range from 0.56 for erythema to 1.00 for worms, with recall rates peaking at 98% for normal findings. This study emphasizes the importance of robust data preprocessing techniques, including normalization and augmentation, in enhancing model performance. The contributions of this work lie in developing an effective AI-driven tool that streamlines the diagnostic process in gastroenterology, ultimately improving patient care and clinical outcomes.
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