Deep convolutional neural networks with ensemble learning and transfer learning for automated detection of gastrointestinal diseases. (November 2022)

• Record Type: Journal Article
• Authors: Su, Qiaosen; Wang, Fengsheng; Chen, Dong; Chen, Gang; Li, Chao; Wei, Leyi
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106054
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Gastrointestinal (GI) diseases are serious health threats to human health, and the related detection and treatment of gastrointestinal diseases place a huge burden on medical institutions. Imaging-based methods are one of the most important approaches for automated detection of gastrointestinal diseases. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to detection of gastrointestinal diseases has not been sufficiently explored. In this study, we propose a novel and practical method to detect gastrointestinal disease from wireless capsule endoscopy (WCE) images by convolutional neural networks. The proposed method utilizes three backbone networks modified and fine-tuned by transfer learning as the feature extractors, and an integrated classifier using ensemble learning is trained to detection of gastrointestinal diseases. The proposed method outperforms existing computational methods on the benchmark dataset. The case study results show that the proposed method captures discriminative information of wireless capsule endoscopy images. This work shows the potential of using deep learning-based computer vision models for effective GI disease screening. Highlights: A novel deep learning framework combining ensemble learning and transfer learning techniques is proposed for classification and fast screening of gastrointestinal (GI) disease images. An ensemble classifier is trained to integrate the information from different base classifiers, achieving more accurate results. Transfer learning is used to improve the accuracy and robustness of the framework. Evaluated on a benchmark dataset, the proposed framework outperforms several existing methods.