• Anusha Kamath
• Dr. Shashidhar Kini K

We detect cervical cancer using machine learning techniques applied to Pap smear image analysis. Through image preprocessing, segmentation, and feature extraction, models such as Convolutional Neural Networks (CNN), Random Forest, and Support Vector Machines (SVM) are employed to classify cervical cell abnormalities. Performance is assessed using metrics including accuracy, sensitivity, specificity, and F1-score. The findings highlight the potential of AI-driven diagnostic systems to enhance early detection, reduce manual diagnostic errors, and support scalable cervical cancer screening. By integrating image-based analysis with advanced algorithms, this approach offers a cost-effective, accessible solution for improving women's health outcomes, particularly in resource-limited settings.

Cervical cancer detection, Pap smear image analysis, machine learning, convolutional neural networks, medical image processing, early diagnosis.

"DETECTION OF CERVICAL CANCER THROUGH PAP SMEAR IMAGES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1175-1179, July-2025, Available :http://www.jetir.org/papers/JETIRGX06227.pdf

"DETECTION OF CERVICAL CANCER THROUGH PAP SMEAR IMAGES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1175-1179, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06227.pdf