融合深度与手工特征，提升胶囊内镜图像分类准确率。
FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification
- 结合CNN、MLP与影像组学，提取多尺度特征
- 在视频帧分类任务中达76.2%验证准确率
- 适合医学图像分类与小样本场景应用
为提升模型准确率、泛化能力及缓解类别不平衡问题，本文提出一种基于特征融合的胶囊内镜图像分类方法。通过结合卷积神经网络（CNN）、多层感知机（MLP）与影像组学，实现丰富且多尺度的特征提取，同时捕捉深层语义与手工设计特征。这些特征被输入分类头进行疾病分类，在胶囊内镜视频帧分类任务中达到76.2%的验证准确率，显著提升模型泛化性与准确性。
原文摘要 · Abstract (English)
In order to improve model accuracy, generalization, and class imbalance issues, this work offers a strong methodology for classifying endoscopic images. We suggest a hybrid feature extraction method that combines convolutional neural networks (CNNs), multi-layer perceptrons (MLPs), and radiomics. Rich, multi-scale feature extraction is made possible by this combination, which captures both deep and handmade representations. These features are then used by a classification head to classify diseases, producing a model with higher generalization and accuracy. In this framework we have achieved a validation accuracy of 76.2% in the capsule endoscopy video frame classification task.
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