Enhanced Deep Bee Colony Optimization for Accurate Classification and Diagnosis of Cervical Cancer

This study presents an innovative approach to automated cervical cancer diagnosis, focusing on the integration of pre-processing, feature extraction, and Deep Bee Colony Optimization (DeepBCO) techniques. The proposed model leverages DeepBCO to enhance cancer detection and classification, ensuring the prevention of early convergence during the optimization process. By effectively utilizing extracted features, DeepBCO supports the accurate classification of medical images, drawing from predefined medical databases. Extensive simulations and evaluations demonstrate that the proposed method significantly outperforms traditional diagnostic techniques in terms of accuracy, particularly in feature extraction and classification tasks. These improvements suggest that the model can provide a reliable, high-performance solution for early cervical cancer detection, potentially benefiting clinical practice by offering faster and more accurate diagnoses. This research highlights the potential of combining deep learning and optimization techniques to advance medical image analysis and improve healthcare outcomes.