Roodgar Amoli Ehsan, Amiri Tehranizadeh Amin, Arabalibeik Hossein
Department of Biomedical Systems & Medical Physics, Tehran University of Medical Sciences, Tehran, Iran.
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
J Biomed Phys Eng. 2025 Aug 1;15(4):369-384. doi: 10.31661/jbpe.v0i0.2301-1590. eCollection 2025 Aug.
Wireless Capsule Endoscopy (WCE) is the gold standard for painless and sedation-free visualization of the Gastrointestinal (GI) tract. However, reviewing WCE video files, which often exceed 60,000 frames, can be labor-intensive and may result in overlooking critical frames. A proficient diagnostic system should offer gastroenterologists high sensitivity and Negative Predictive Value (NPV) to enhance diagnostic accuracy.
The current study aimed to establish a reliable expert diagnostic system using a hybrid classification approach, acknowledging the limitations of individual deep learning models in accurately classifying prevalent GI lesions. Introducing a hybrid classification framework, ensemble learning techniques were applied to Deep Convolutional Neural Networks (DCNNs) tailored for WCE frame analysis.
In this analytical study, DCNN models were trained on balanced and unbalanced datasets and then applied for classification. A model scoring hybrid classification approach was used to create meta-learners from the DCNN classifiers. Class scoring was utilized to refine decision boundaries for each class within the hybrid classifiers.
The VG_BFCG model, constructed on a pre-trained VGG16, demonstrated robust classification performance, achieving a recall of 0.952 and an NPV of 0.977. Tuned hybrid classifiers employing class scoring outperformed model scoring counterparts, attaining a recall of 0.988 and an NPV of 1.00, compared to 0.979 and 0.989, respectively.
The unbalanced dataset, with a higher number of Angiectasia frames, enhanced the classification metrics for all models. The findings of this study underscore the crucial role of class scoring in improving the classification metrics for multi-class hybrid classification.
无线胶囊内镜检查（WCE）是胃肠道（GI）无痛且无需镇静可视化的金标准。然而，查看通常超过60000帧的WCE视频文件可能会耗费大量人力，并且可能导致忽略关键帧。一个高效的诊断系统应该为胃肠病学家提供高灵敏度和阴性预测值（NPV），以提高诊断准确性。
当前研究旨在使用混合分类方法建立一个可靠的专家诊断系统，认识到个体深度学习模型在准确分类常见GI病变方面的局限性。引入混合分类框架，将集成学习技术应用于为WCE帧分析量身定制的深度卷积神经网络（DCNN）。
在这项分析研究中，DCNN模型在平衡和不平衡数据集上进行训练，然后应用于分类。使用模型评分混合分类方法从DCNN分类器创建元学习器。类别评分用于细化混合分类器中每个类别的决策边界。
基于预训练的VGG16构建的VG_BFCG模型表现出强大的分类性能，召回率达到0.952，NPV为0.977。采用类别评分的调优混合分类器优于采用模型评分的同类分类器，召回率达到0.988，NPV为1.00，而模型评分的同类分类器召回率和NPV分别为0.979和0.989。
具有较多血管扩张帧的不平衡数据集提高了所有模型的分类指标。本研究结果强调了类别评分在改善多类混合分类的分类指标方面的关键作用。
