ItemConference Paper

An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization

Title
An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization
Creator
Moorthy, C.; Shafeek, A.; Paul, Eldho; Madhan, S.; Eswar, D.; Naveenkumar, R.
Description
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. 2025 IEEE.
Source
Proceedings of the 9th International Conference on Inventive Systems and Control, ICISC 2025;pp.715-722
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Cervical Cancer; CNN; Deep Learning; Lion Optimizer; Medical Image Segmentation; Pap Smear; U-Net
Coverage
Moorthy C., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Shafeek A., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Paul E., Electronics and Communication Engineering Christ(Deemed to Be University), Bangalore, India; Madhan S., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Eswar D., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Naveenkumar R., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151247-7;
Format
online
Language
English
Type
Conference paper

Citation

Moorthy, C.; Shafeek, A.; Paul, Eldho; Madhan, S.; Eswar, D.; Naveenkumar, R., “An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26045.

Output Formats