用EfficientNet-b7改进胶囊网络，自动识别胃肠道10类异常
CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7
- 基于预训练EfficientNet-b7构建胶囊网络，融合PReLU激活函数
- 在高度不平衡数据上微调后，验证集微平均准确率达84.5%
- 适合对推理速度敏感的临床辅助诊断场景
胃肠道（GI）疾病是全球重大健康问题，胶囊内镜（CE）通过捕捉大量胃肠道图像提供无创诊断方式。然而，视频帧数量庞大，需自动化分析以减轻医生负担并提升诊断准确率。本文提出CapsuleNet，用于2024年胶囊视觉挑战赛，旨在分类10种不同胃肠道异常。针对高度不平衡数据集，采用多种数据增强策略降低数据偏差。模型基于预训练EfficientNet-b7主干网络，添加分类层并使用PReLU激活函数优化。在验证集上表现优异，微平均准确率达到84.5%，在多数类别上优于VGG16基线。尽管如此，某些异常如Erythema仍存在分类困难。研究表明，基于CNN的CapsuleNet可为胃肠道疾病分类提供高效解决方案，尤其适用于推理时间关键的应用。
原文摘要 · Abstract (English)
Gastrointestinal (GI) diseases represent a significant global health concern, with Capsule Endoscopy (CE) offering a non-invasive method for diagnosis by capturing a large number of GI tract images. However, the sheer volume of video frames necessitates automated analysis to reduce the workload on doctors and increase the diagnostic accuracy. In this paper, we present CapsuleNet, a deep learning model developed for the Capsule Vision 2024 Challenge, aimed at classifying 10 distinct GI abnormalities. Using a highly imbalanced dataset, we implemented various data augmentation strategies, reducing the data imbalance to a manageable level. Our model leverages a pretrained EfficientNet-b7 backbone, tuned with additional layers for classification and optimized with PReLU activation functions. The model demonstrated superior performance on validation data, achieving a micro accuracy of 84.5% and outperforming the VGG16 baseline across most classes. Despite these advances, challenges remain in classifying certain abnormalities, such as Erythema. Our findings suggest that CNN-based models like CapsuleNet can provide an efficient solution for GI tract disease classification, particularly when inference time is a critical factor.
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