A Comprehensive Analysis on Cervical Cancer Detection Using Neural Networks
S Sanathkumar., S. Raja · 2025
Among the many cancers that afflict females, cervical cancer ranks high. Thousands of women are diagnosed with cervical cancer each year. Early detection of abnormal precancerous tumours helps to prevent cervical cancer. In a cervical cell picture including thousands of cells, the presence or absence of abnormal nuclei is determined by the precise diagnosis made by the clinician or pathologist. In this survey paper analyze 32 research papers regarding the cervical cancer with comparative analysis. In this survey analyzed the importance of cervical cancer prediction and machine learning oriented papers also analyzed. As a result of pathologists and clinicians must rely on a quick and exact detection approach. Conventional detection systems typically consist of two essential processes: segmentation and classification. Eventually, the abnormal sets undergo a final diagnostic search. Computerassisted cervical cancer screening technologies have evolved dramatically in recent years. Correct cell segmentation is critical for the efficacy of a cervical cell screening approach the existing methods are Fuzzy C-Means (FCM), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) were discussed with diagnose of cervical cancer.