Automatic Classification of Cervical Cells Using Deep Learning Methods

Abstract

Cervical cancer, second only to breast cancer, is one of the cancers leading to cause death among women. Cervical cancer is a cancer that forms in the cells of the cervix, which is the lower section of the uterus that connects the uterus to the pelvis to the vaginal area. Various forms of the papilloma virus (HPV), a sexually transmitted infection that plays a role in cervical cancer and plays a critical part in most cases. The risk of cervical cancer developing can be reduced by undergoing screenings and receiving a vaccination that protects against HPV infection Cancer prevention is important. Most of the time, this is accomplished by checking the transformation zones. Cervical pre-cancerous stages can be observed in three different types, and all can transfigure into cancer. As a result, it's crucial to screen cervical anomalies sensibly and have a reliable process to determine if a cervix is normal (healthy) or pre-cancerous. Presently, the test being carried is a Pap smear test, commonly referred as a Pap test, which is a cervical screening procedure. It examines the cervix for the presence of pre - cancerous or cancerous cells. At present times deep learning is becoming more important alternative for cancer screening. A cervical cancer detection and classification system based on CNN has been proposed. Deep-learned features are acquired using the CNNs mode