Abstract
Wireless capsule endoscopy (WCE), the most efficient technology, is used in the endoscopic department for the examination of gastrointestinal (GI) diseases such as a poly and ulcer. WCE generates thousands of frames for a single patient???s procedure, and the manual examination is time-consuming and exhausting. In the WCE frames, computerized techniques make the manual inspection process easier. Deep learning has been used by researchers to introduce a variety of techniques for the classification of GI diseases. Some of them have concentrated on ulcer and bleeding classification, while others have classified ulcers, polyps, and bleeding. In this paper, we proposed a deep learning and Moth-Crow optimization-based method for GI disease classification. There are a few key steps in the proposed framework. Initially, the contrast of the original images is increased, and three operations based on data augmentations are performed. Then, using transfer learning, two pre-trained deep learning models are fine-tuned and trained on GI disease images. Features are extracted from the middle layers using both fine-tuned deep learning models (average pooling). On both extracted deep feature vectors, a hybrid Crow-Moth optimization algorithm is proposed and applied. The resultant selected feature vectors are later fused using the distance-canonical correlation (D-CCA) approach. For classifying GI diseases, the final fused vector features are classified using machine learning algorithms. The experiments are carried out on three publicly available datasets titled CUI Wah WCE imaging, Kvasir-v1, and Kvasir-v2, providing improved accuracy with less computational time compared with recent techniques.