• S V RAJESH KUMAR
• G NEERAJA
• G MALLIKARJUNA
• M NIKHIL
• M MANJUNATH,S MUNI SANKAR

Cervical cancer (CC) ranks as the fourth most common malignant tumor among women globally. Developing a highly accurate deep convolutional neural network (DCNN) for cervical cancer screening and diagnosis is crucial for effective prevention. In this study, we propose a robust DCNN for cervical cancer screening, utilizing whole-slide images (WSI) of ThinPrep cytologic test (TCT) slides from various patients. Employing an active learning strategy, we enhance the efficiency and accuracy of image labeling. The best model demonstrates superior sensitivity, specificity, and accuracy for CC patient identification compared to traditional supervised learning methods. Our findings highlight the effectiveness of the active learning strategy in reducing costs and improving image labeling quality.

Cervical cancer prediction, Machine learning. Sensitivity, Personalized treatment, Early diagnosis.

"A MODEL FOR PREDICTING CERVICAL CANCER USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g239-g245, April-2024, Available :http://www.jetir.org/papers/JETIR2404628.pdf

"A MODEL FOR PREDICTING CERVICAL CANCER USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg239-g245, April-2024, Available at : http://www.jetir.org/papers/JETIR2404628.pdf