Revolutionizing Cervical Cancer Diagnosis Through Ai A Deep Learning Framework for Accurate Detection Classification and Segmentation

Abstract

Cervical cancer is due to the proliferation of cells in the cervix or subordinate section of the uterus that connects to the vagina, result in an uncontrolled growth of the tissue. Incidence, and especially mortality caused by cervical cancer, is greater in low-income nations than in developed countries [1]. Cervical cancer is fairly common after breast cancer among women in many low-income countries. Most cases of cervical cancer arise in the transition zone, the area where the endocervix meets the exocervix. Cervical cancer, however, develops only in this zone, so a woman who has ever been in this zone is more likely as she goes on age to get a cervical cancer [2]. Women already with the disease in their family are at a greater risk of developing cancer in the cervix. Cancer does not form right away. The immune system of a woman of childbearing age takes 15 - 20 years to mature, but a weakened immune system can mature in 5 - 10 years. The incidence of both morbidity and mortality from cancer requires cancer to be detected at an early stage. newlineThere is ample evidence that early treatment can save up to 80% of the people, compared to the world survival rate of 48%. The high cost of vaccination and screening keeps cervical cancer detection services out of the reach of many women residing in many the impoverished countries and as a result, these countries have high mortality rates [3]. Luckily, advanced computational techniques using publicly available datasets like SipakMed, Herlev Dataset and Liquid Based Cytology Pap Smear Dataset have contributed significantly in early detection and diagnosis of the disease. These images are high resolution cytology images, which provide the opportunity to develop AI driven models to classify cervical cancer automatically. Through deep learning method such datasets allow to make precise feature extraction and classification of cervical cell abnormalities and their high diagnosis accuracy and dissemination in resource constrained settings newline