A low-cost high-accuracy method for colorectal polyp detection

A colorectal polyp is a small lesion that appears on the mucosal surface of the colon. Although many polyps are harmless, some can progress into colorectal cancer, becoming dangerous and potentially fatal when detected at advanced stages. Most existing research on polyp detection relies on deep learning methods, which, despite their effectiveness, suffer from several limitations, including high energy consumption, costly hardware requirements, and concerns related to privacy, security, and ethics. The proposed methodology introduces a low-cost alternative by separating each two-dimensional (2D) endoscopic image into its red, green, and blue (RGB) channels and converting each channel into a three-dimensional (3D) representation. A specialized colormap is applied to the 3D images, after which the colormap outputs of the green and blue layers are converted into binary images. Subtracting the colormap-based red layer from this combined binary image produces a mask that effectively isolates the polyp region. The method achieved strong results across three benchmark datasets. Recall reached 97% on CVC-ClinicDB, 95.5% on Kvasir-SEG, and 95.3% on CVC-ColonDB. Precision values were 96.5%, 93.4%, and 93.6%, respectively. F1-scores ranged from 94.5% to 95.3%. This approach offers an efficient, accessible solution suitable for devices with limited computational resources.