Enhancing Cervical Cancer Screening with a Resnet-Based Approach and Interpretability Techniques

Cervical cancer is the fourth most prevalent cancer among women, with over 600,000 new cases and 300,000 deaths reported annually. While early detection through Pap smear screening significantly reduces mortality, traditional methods are labor-intensive and heavily reliant on expert cytologists, creating barriers in resource-limited settings. This study proposes a cervical cancer screening through a Resnet-based method and interpretability technique (Inter-ResNet-CxCa) for classifying Pap smear cell images into three categories: unhealthy cells, healthy cells, and rubbish. By using pre-trained models, transfer learning lowers computational demands, mitigates overfitting, and enhances performance on task-specific datasets. To improve interpretability, we incorporate Local Interpretable Model-Agnostic Explanations (LIME), offering visual insights into model predictions. This model has the potential to enhance cervical cancer screening, particularly in resource-limited settings.