• Mrs.SIVASAKTHI S

Cervical cancer is one of the most prevalent and deadly diseases that affect women. In contrast to other malignancies, it has no symptoms in the early stages, which increases the death rate in women. Transitioning from the precancerous to the severe stage takes 8 to 10 years. The main causes of increased cervical cancer rates in underdeveloped nations include a lack of resources, ineffective screening programmes, and poorly organized health systems designed to identify precancerous conditions before they develop into persistent cancer. As a result, effective cervical cancer screening programmes require a low-cost methodology. Reduced death rates are achieved with early detection and classification of cervical cancer. Images from Pap smears are frequently used for automated cervical cancer detection, resulting in reliable and accurate results. Recently, various image-processing methods have been applied to detect cervical cancer. Colposcopy and the Pap test are the two ways to acquire cells; however, because of their inexpensive cost and painless diagnosis, Pap smear tests are more popular. Both machine learning and deep learning approaches have been used in earlier studies.

Cervical Cancer – Pap smear - Image Segmentation – Classification

"PREDICTING AND CLASSIFYINGTHE INITIAL STAGE CERVICAL CANCER BASED ON DEEP LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.211-219, March-2023, Available :http://www.jetir.org/papers/JETIRFV06041.pdf

"PREDICTING AND CLASSIFYINGTHE INITIAL STAGE CERVICAL CANCER BASED ON DEEP LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. pp211-219, March-2023, Available at : http://www.jetir.org/papers/JETIRFV06041.pdf