Abstract: It underlines the rather important role that cervical cancer plays in being a major cause of deaths from cancer among women around the world, not necessarily because of other reasons it progresses slowly and often unpredictably. Early detection through screening forms the basic steps involved in the prevention of cervical cancer and this involves identification and monitoring of precancerous zones within the cervix, which again can be categorized into three different types namely, type 1, type 2, and type 3. Proper identification and analysis of every one of these stages can effectively check their progression into invasive cancer. Such appeals for accurate classification of cervical pre- cancerous images into these categories through highly advanced automated systems. The intelligent system through artificial intelligence as well as machine learning is designed to improve efficiency and precision in the cervical cancer screening so that timely intervention is facilitated. Systems focused on the more individualized and targeted approach tend to prevent the precancerous cells from being transformed into cancerous cells. Automated tools provide a reliable alternative in resource constraint settings whereby the screening process done through manual tools is not in place, and such a screening becomes possible with fewer rates of error in diagnosing it, which also happens to be accessible since a deep model like ResNet-50 generates notable performance in the colposcopy image classification that improves the cervical cancer screening as well as the preventive measures in place. Such discoveries promise a new direction in the treatment of cervical cancer and significantly fewer deaths from that disease, therefore ensuring improved overall results for women's health worldwide.
Keywords: Cervical cancer screening, Colposcopy images, Deep learning, Diagnostic accuracy, Early detection, Machine learning, Pre-cancerous zones, ResNet-50, Screening.
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DOI:
10.17148/IJARCCE.2025.14428
