CSO — VGG-19: Prediction of Cervical Cancer Using Cuckoo Search-Based Deep VGG-19
M. Suresh Anand, S. Manimozhi, Md. Abul Ala Walid, A. Naresh Kumar, P. Dharmendra Kumar, N.V.S. Suryanarayana · 2023
Nearly four percent of women worldwide have cervical cancer, which can be fatal if not detected and treated early. When contrasted to current numbers, the death rate a few decades ago was unacceptably high. In recent years, clinics have intended to employ advancements in digital image and machine learning to improve cervical cancer screening. Women that test negative have a low risk of developing cancer of the cervical cavity over the following ten years, even if the majority of cervical infections that result in positive testing do not cause precancer. For the prompt identification as well as prompt cure for cervical cancer, it is crucial to identify important genes in cancer, identify healthy people as cervical cancer predictors, and perform an initial evaluation of a high-through expression information. The major objective of the suggested effort is to identify cervical cancer in order to forecast cervical genes. To find the cervical genes, a cuckoo-based deep conversion is created. To some degree, the proposed model predicts the progression of cervical cancer using the VGG-19 model. The deep conversion-based cuckoo search technique can be used to identify cervical cancer patients. The proposed model obtains a 99.30% accuracy rate.