Abstract
The rising global population, lifestyle changes, and altered dietary habits have contributed to an increase in gastrointestinal diseases, including colon cancer, necessitating robust computer-aided diagnosis (CAD) systems for early detection and clinical decision-making. This paper proposes a deep semantic segmentation–assisted hybrid deep-ensemble framework (DSH-DeNet) for colon cancer screening using endoscopic images. The proposed framework applies preprocessing and UNet-based region-of-interest (ROI) segmentation to localize salient colorectal regions and enhance feature extraction from endoscopic images. Deep features are extracted using EfficientNet-B7, MobileNet-V2, and DenseNet121, followed by PCA-based feature refinement and z-score normalization. An ensemble-of-ensemble (E2E) learning strategy is employed for robust multi-class endoscopic image classification. With 98.99% accuracy, 99.24% precision, 99.17% recall, and an F-measure of 0.99, the experimental evaluation shows excellent classification performance. The results show that the suggested segmentation-assisted framework for computer-aided colon cancer screening utilizing endoscopic images is successful.