Optimized Deep Belief Network for Colorectal Cancer Detection Using Hybrid PIO-DE Algorithm

Colorectal cancer (CRC) remains one of the most prevalent and deadly forms of cancer, necessitating advanced detection methods to improve patient outcomes. Traditional diagnostic techniques, such as colonoscopy and biopsy, are invasive and time-consuming, highlighting the need for automated, accurate diagnostic tools. This chapter introduces a groundbreaking approach that leverages Deep Belief Networks (DBNs) optimized by a hybrid Pigeon-Inspired Optimization (PIO) and Differential Evolution (DE) algorithm. The hybrid PIO-DE algorithm combines the global search capabilities of PIO with the local search efficiency of DE, resulting in a robust optimization strategy that enhances the feature extraction and classification performance of DBNs. The chapter details the preprocessing of medical images, the architecture of DBNs, and the optimization process, providing a thorough exploration of how this hybrid approach can improve the accuracy and speed of CRC detection. Experimental results demonstrate the superior performance of the proposed method compared to existing optimization techniques, underscoring its potential to revolutionize medical image analysis and cancer diagnosis