Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Abstract

Cervical cancer remains a significant global health concern, demanding accurate and efficient diagnostic approaches for early detection and treatment. However, there are situations where a single deep learning model might not be sufficient to adequately capture the pertinent information required for precise disease prediction from intricate datasets. In this paper, we introduce an innovative ensemble learning framework that leverages the Differential Evolution (DE) algorithm to enhance the detection of cervical cancer. Our innovative approach combines the collective strength of a diverse set of base models using feature fusion techniques to achieve precise and robust classification. We have developed three base models, denoted as Fusion 1, Fusion 2, and Fusion 3, where each fusion model incorporates two pre-trained models using a feature fusion technique. To capture sequential dependencies and improve feature attention, we have integrated ConvLSTM layers and Squeeze-and-Excitation (SE) blocks into these base models. Subsequently, we've amalgamated the predictions of each base model using a weighted aggregation scheme, with the weights optimized using the DE algorithm. Our ensemble framework introduces a novel method for dynamic weight adjustment, utilizing advanced fusion techniques to improve cervical cancer detection accuracy. This approach ensures robust diagnostic performance across cancer cells datasets, enhancing patient outcomes and community health. Our evaluation covers two datasets, SIPaKMeD and Mendeley LBC, demonstrating the remarkable performance and robustness of our ensemble model. It consistently outperforms individual base models and even surpasses state-of-the-art methods, achieving an impressive accuracy rate of 98.96 % on the Mendeley LBC dataset and 95.68 % on the SIPaKMeD dataset. The incorporation of ConvLSTM layers, SE blocks, and DE optimization significantly contributes to the model's exceptional accuracy, its ability to generalize effectively, and its adaptability to diverse scenarios. Additionally, we employ Grad-CAM to visualize how the base models focus on crucial regions for accurate predictions, providing insights into the model's decision-making process. Our ensemble model exhibits outstanding Area under the Curve (AUC) values in Receiver Operating Characteristic (ROC) analysis, further confirming its effectiveness. This research represents a significant advancement in cervical cancer detection, underscoring the potential of ensemble learning techniques optimized through DE for enhancing diagnostic accuracy and advancing patient care.