A Novel Ensemble Approach for Colon Cancer Detection Over the Multiclass Colon Dataset

A successful system is required for image processing, analysis, and early detection of colon cancer, as it has become the second most serious type of cancer, affecting approximately 15% of people worldwide. Therefore, the colon cancer prediction system utilizes MRI imaging to forecast the onset of colon cancer. To effectively investigate colon cancer at its initial stages, both high and low-level characteristics are essential throughout the procedure. In this study, we propose an ensemble approach for detecting colon cancer using a multiclass colon dataset. This paper demonstrates that the suggested ensemble approach achieves an accuracy score of 98.35% and respective precision, recall, and F1-scores of 98%. Importantly, the proposed ensemble approach exhibits superior detection accuracy in comparison to existing methods, offering an improved screening approach that can significantly reduce the pathologist’s workload in identifying tumor regions. This innovative method holds the potential to serve as a robust tool for enhancing colon cancer diagnostics.