Early Detection And Categorization Of Cervical Cancer Cells Using Smoothing Cross Entropy-Based Multi-Deep Transfer Learning

Abstract

Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in developing regions where access to skilled pathologists and advanced diagnostic facilities is limited. Manual examination of Pap smear slides is time-consuming, subjective, and prone to inter-observer variability, which can lead to delayed or incorrect diagnosis. Recent advancements in deep learning and computer vision have shown significant promise in automating medical image analysis, thereby improving diagnostic accuracy and efficiency. This project presents a web-based intelligent medical system for the early detection and categorization of cervical cancer cells using deep transfer learning techniques. The proposed system leverages EfficientNet-based transfer learning combined with smoothing cross entropy loss to enhance generalization and reduce overfitting during multi-class classification of Pap smear images. Automated image preprocessing, model training, evaluation, and real-time prediction are integrated into a unified platform. The system provides a secure, scalable, and user-friendly environment that supports early diagnosis, improves clinical decision support, and demonstrates the practical application of artificial intelligence in healthcare diagnostics.