A Deep Learning Approach for Cervical Cancer Prediction and Prevention in Women’s Health

Cervical cancer remains a significant health concern worldwide, especially in regions with limited access to healthcare resources. Early detection and prevention are crucial for reducing the burden of this disease. In recent years, deep learning has emerged as a promising tool for analyzing medical data and improving diagnostic accuracy. This article explores the application of deep learning techniques in predicting and preventing cervical cancer. Pap smear screening is used to screen for cervical cancer in order to diagnose and categorize the disease. To identify and categorize cervical tissue abnormalities, Pap smear pictures of the cervical region are used. We presented a deep learning-based approach in this paper that can categorize pap smear images into different classifications. A computer-aided diagnosis method that classifies abnormalities in cervical imaging cells is designed using Pap smear images. Seven image classes are discriminated using automated features that were derived using ResNet101. The ability of the proposed approach to differentiate between the usual case levels with 95.02sensitivity and 95.93% accuracy. Furthermore, it has a 95.93 accuracy rate in differentiating between normal and a typical instance. After that, the high degree of anomaly is examined and accurately classified.