Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.

In order to address the challenges posed by the large number of images acquired during wireless capsule endoscopy examinations and fatigue-induced leakage and misdiagnosis, a deep ensemble framework is proposed, which consists of CA-EfficientNet-B0, ECA-RegNetY, and Swin transformer as base learners. The ensemble model aims to automatically recognize four lesions in capsule endoscopy images, including angioectasia, bleeding, erosions, and polyps. All the three base learners employed transfer learning, with the inclusion of attention modules in EfficientNet-B0 and RegNetY for optimization. The recognition outcomes from the three base learners were subsequently combined and weighted to facilitate automatic recognition of multi-lesion images and normal images of the gastrointestinal (GI) tract. The weights were determined through the Bayesian optimization. The experiment collected a total of 8358 images of 281 cases at Shanghai East Hospital from 2017 to 2021. These images were organized and labeled by clinicians to verify the performance of the algorithm. The experimental results showed that the model achieved an accuracy of 84.31%, m-Precision of 88.60%, m-Recall of 79.36%, and m-F1-score of 81.08%. Compared to mainstream deep learning models, the ensemble model effectively improves the classification performance of GI diseases and can assist clinicians in making initial diagnoses of GI diseases.