Early Colon Cancer Prediction from Histopathological Images Using Enhanced Deep Learning with Confidence Scoring

Colon Cancer (CC) arises from abnormal cell growth in the colon, which severely impacts a person's health and quality of life. Detecting CC through histopathological images for early diagnosis offers substantial benefits in medical diagnostics. This study proposes NalexNet, a hybrid deep-learning classifier, to enhance classification accuracy and computational efficiency. The research methodology involves Vahadane stain normalization for preprocessing and Watershed segmentation for accurate tissue separation. The Teamwork Optimization Algorithm (TOA) is employed for optimal feature selection to reduce redundancy and improve classification performance. Furthermore, the NalexNet model is structured with convolutional layers and normal and reduction cells, ensuring efficient feature representation and high classification accuracy. Experimental results demonstrate that the proposed model achieves a precision of 99.9% and an accuracy of 99.5%, significantly outperforming existing models. This study contributes to the development of an automated and computationally efficient CC classification system, which has the potential for real-world clinical implementation, aiding pathologists in early and accurate diagnosis.