utilizing deep convolutional neural networks and hybrid classification for gastrointestinal disease diagnosis from capsule endoscopy images

Background: 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.objective: 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.material and methods: 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.results: 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. conclusion: 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.