Enhancing Gastrointestinal Disease Detection through Augmented Deep Learning

Abstract

In recent years, deep learning has become a cornerstone of advancements in medical imaging, facili- tating significant improvements in early disease detection. This study presents a phased approach to optimizing gastrointestinal (GI) disease diagnostics by implementing a structured data augmentation and preprocessing phase. Leveraging an expanded dataset with novel clinically rele- vant classes, this phase seeks to increase model robustness and classification accuracy. Our methodology employs targeted data augmentation techniques coupled with Ef- ficientNetV2 for detailed feature extraction in endoscopic imagery. Initial results underscore the potential for sub- stantial improvements in diagnostic precision, particularly in identifying nuanced GI conditions. By focusing on this foundational phase, this work establishes a framework for developing advanced AI-driven diagnostic tools tailored for GI disease detection.