Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification

TL;DR

CNN architecture designed specifically for multiclass classification of ten gut pathologies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms as well as their normal state are presented.

Abstract

In recent years, the diagnosis of gastrointestinal (GI) diseases has advanced greatly with the advent of high-tech video capsule endoscopy (VCE) technology, which allows for non-invasive observation of the digestive system. The MisaHub Capsule Vision Challenge encourages the development of vendor-independent artificial intelligence models that can autonomously classify GI anomalies from VCE images. This paper presents CNN architecture designed specifically for multiclass classification of ten gut pathologies, including angioectasia, bleeding, erosion, erythema, foreign bodies, lymphangiectasia, polyps, ulcers, and worms as well as their normal state.

Figures (5)

• Figure 1: Flowchart diagram.
• Figure 2: Accuracy and Loss result for model training
• Figure 3: Hyperparameter Table for model training
• Figure 4: Table result for model training
• Figure 5: Class metrics for model training