Colorectal Cancer Detection Using Pre-Trained Ensemble Algorithms

Abstract

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality
worldwide, where early detection plays a critical role in improving treatment outcomes and
patient survival rates. Conventional diagnostic methods, such as colonoscopy and
histopathological analysis, while effective, are often invasive, time-consuming, and
susceptible to human error. Recent advancements in machine learning and artificial
intelligence, particularly ensemble learning techniques, provide promising solutions for
automated and reliable cancer detection. This project proposes a robust colorectal cancer
detection framework based on pre-trained ensemble algorithms to improve diagnostic
performance and reduce detection time. The proposed system integrates multiple advanced
models, including Random Forest, Gradient Boosting, and XGBoost, and fine-tunes them on
medical datasets to enhance predictive capability. Experimental results demonstrate that the
ensemble-based approach achieves a detection accuracy of 96.8%, outperforming individual
models in identifying cancerous patterns from both structured and image-based data. The
proposed framework highlights the potential of ensemble learning for developing efficient,
accurate, and scalable colorectal cancer diagnosis systems