AutoEffFusionNet: A New Approach for Cervical Cancer Diagnosis Using ResNet-based Autoencoder with Attention Mechanism and Genetic Feature Selection

Abstract

Cervical cancer poses a significant global health challenge, necessitating accurate and efficient diagnostic solutions. This study introduces a novel hybrid framework, AutoEffFusionNet, that integrates unsupervised feature learning through ResNet-based autoencoders with attention mechanisms and supervised learning via transfer learning models. By leveraging the complementary strengths of these approaches, the proposed method achieves enhanced diagnostic accuracy in cervical cancer classification. Genetic algorithms optimize the feature selection process, retaining only the most relevant attributes, thereby addressing feature redundancy and improving computational efficiency. The selected features are then classified using a Support Vector Machine, effectively combining deep learning's feature extraction capabilities with machine learning's robust classification strengths. Additionally, Grad-CAM visualizations are incorporated to highlight critical regions influencing the classification decisions, enhancing interpretability and transparency. The framework was rigorously evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, achieving remarkable accuracies of 99.26% and 100%, respectively. These results demonstrate the effectiveness of the proposed model in addressing key challenges in cervical cancer diagnosis and its potential for deployment in clinical applications.