An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization
Moorthy C, A Shafeek, Eldho Paul, S Madhan, D Eswar, R. Naveenkumar · 2025
One of the leading causes of death for women is cervical cancer, and survival rates are significantly increased by early identification using Pap smear analysis. However, manually reviewing Pap smear images takes time and is prone to mistakes. Using an enhanced Convolutional Neural Network (CNN) with Transformer-like classification and U-Net-based nucleus segmen-tation, this study suggests an automated cervical cancer detection system. To increase feature extraction, the image is preprocessed using techniques such as edge detection, contrast enhancement with CLAHE, and greyscale conversion. The PR-processed image is segmented using U Net segmentation. A YoLo-based CNN optimised with the Lion optimiser (Evolved Sign Momentum) is used to classify the segmented nuclei to improve convergence and accuracy. Results from experiments show that our model outperforms con-ventional methods in terms of classification accuracy. By lowering reliance on manual screening and increasing early diagnosis rates, this automated approach can help medical practitioners detect cervical cancer more quickly and accurately.