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.

翻译：本文提出了一种深度学习框架，用于视频胶囊内窥镜影像中胃肠道异常的多类别分类。其目标是自动识别十种胃肠道异常类别，包括血管扩张、出血和溃疡等，从而减轻胃肠病学专家的诊断负担。通过集成DenseNet与ResNet架构，所提出的模型在结构良好的数据集上实现了94%的整体准确率。精确率得分从红斑的0.56到蠕虫的1.00不等，召回率在正常发现类别中达到98%的峰值。本研究强调了包括归一化与数据增强在内的鲁棒数据预处理技术对提升模型性能的重要性。本工作的贡献在于开发了一种有效的人工智能驱动工具，可优化胃肠病学诊断流程，最终改善患者护理与临床疗效。