Deep Learning Based Cervical Cancer Detection - Muruganantham, U; Palanivel, N; Mathew, E.Benny; arul, A.Jerald; Kaalasthiyan, M.; rajan, D.Dhinesh (Journal of Neonatal Surgery, 2025)
Cervical cancer remains a significant global health concern, ranking among the leading causes of cancer-related deaths in women. Early detection through advanced screening techniques can drastically improve survival rates. This study presents a deep learning-based approach utilizing EfficientNet B0, a powerful yet lightweight convolutional neural network (CNN), to classify cervical cancer stages based on histopathological images. The model categorizes images into four classes: HSIL, LSIL, Normal (NL), and SCC, ensuring precise and reliable classification. The trained model is integrated into a Flask web application, allowing users to upload cervical cell images for real-time diagnosis. Upon classification, the application provides stage-specific medical guidance to support early intervention. The system enhances cervical cancer detection by offering an automated, accessible, and efficient diagnostic tool. Future enhancements may include real-time image processing and integration with telemedicine platforms to broaden its clinical applications.