A survey on deep learning models for wireless capsule endoscopy image analysis

Abstract

Abdomen Bleeding, Ulcer, Tumour, Crohn's disease, Celiac disease and other diseases in the gastrointestinal tract (GI) are difficult to diagnose, due to the inescapable inherent difficulty in accessing a volute setting in the human body. Wireless Capsule Endoscopy (WCE) offers a patient-cordial, non-invasive and painless investigation in the GI tract. Automatic detection of anomalies in WCE images using Deep Learning Models improves the detection accuracy but it requires a huge number of labeled data for model training. But these deep models suffer from explain-ability and fail to include expert knowledge in the model decision-making process. By keeping these aspects in mind, this survey aims to identify the opportunities for using Semi-Supervised deep learning models over supervised deep learning methods in Wireless Capsule Endoscopy (WCE) anomaly detection and classification. This paper presents a comprehensive survey on various deep learning solutions for anomaly detection and localization techniques utilized in WCE images in the aspect of performance, complexity, and the quality of the dataset. The survey outlined the proposed Attention and Domain Assisted Generative Adversarial Network (ADA-GAN) based Semi-Supervised Model for WCE anomaly image classification along with initial results. The result derives the shortcomings of the current literature methods and paves the potential research opportunities in the Semi-Supervised models in Wireless capsule endoscopy image analysis.