Erdal Akın

Abstract Medical image analysis plays a crucial role in the accurate diagnosis of gastrointestinal disorders. The Kvasir dataset, which consists of endoscopic images annotated by experienced medical professionals, serves as a valuable resource for developing advanced machine learning models. This study introduces a novel hybrid methodology that enhances classification performance by leveraging transformer-based models, deep feature extraction, and optimization techniques.