A Two-Stage Framework for Abnormalities Detection in WCE Images by Combining Semantic Segmentation and Deformable Agent-Based Classification

Wireless capsule endoscopy (WCE) has revolutionized gastrointestinal (GI) diagnostics by offering a patient-friendly imaging and diagnostic tool compared to traditional endoscopic techniques. However, the manual assessment of these images is a time-consuming task and is prone to inaccuracies, which necessitates the implementation of automated approaches. In this paper, we introduce a two-stage deep learning framework to identify the most common GI abnormalities in WCE images. The first stage in the proposed method is to segment suspicious regions from the WCE images, which act as potential markers for GI abnormalities. In the second stage we perform frame-level classification to identify and categorize different pathologies in the GI tract. Extensive experiments conducted on four image datasets demonstrate that our approach achieves the highest values in terms of accuracy, precision, recall and specificity in comparison with four common deep learning methods : Resnet50, VGG16, Vit-S16 and InceptionV3.