Enhancing transformer-based architectures with geometric deep learning for colonoscopic polyp size classification using transfer learning.

Abstract

Accurate estimation of polyp size during colonoscopy is critical for risk assessment and surveillance planning in colorectal cancer prevention. However, current methods often rely on subjective visual judgment, leading to inconsistencies and potential misclassification. This study proposes a deep learning framework that enables automated and objective polyp size classification by integrating RGB and depth information. The approach leverages a modified Af-SfM module to generate refined and rectified depth maps, which are combined with RGB inputs to support classification into clinically relevant size categories. The model was trained and validated on a dataset of over 10,000 annotated colonoscopic images curated by expert gastroenterologists. Experimental results demonstrate that incorporating rectified depth information significantly improves classification performance over RGB-only baselines. For polyps measuring 10 mm or larger, the system achieved a precision of 91.5% and a recall of 93.6%. These findings highlight the potential of depth-enhanced deep learning methods to support more consistent and accurate polyp size estimation in clinical endoscopy.