标题： FUSECAPS：基于特征融合的胶囊内窥镜图像分类框架研究 显示英文标题

标题： FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification

摘要： 为了解决提高模型准确性、泛化能力以及类别不平衡的问题，本研究提出了一种用于内窥镜图像分类的强大方法。 我们建议结合卷积神经网络（CNN）、多层感知器（MLP）和影像组学的混合特征提取方法。 这种组合使得丰富的多尺度特征提取成为可能，能够捕捉深度学习特征和手工设计特征。 随后，这些特征被分类头用于疾病分类，从而生成具有更高泛化能力和准确性的模型。 在此框架下，我们在胶囊内窥镜视频帧分类任务中达到了76.2%的验证准确率。 显示英文摘要

摘要： 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.