Detection of Cervical Cancer using Colposcopy: An Image Processing Approach

The fourth most frequent disease in women globally is cervical cancer, and effective treatment depends on early detection. Colposcopy, which involves visually inspecting the cervix under a specialized microscope, is a frequently employed technique for identifying cervical cancer. In this study, we suggest a colposcopy image-based computer-aided diagnostic (CAD) system for the quick and painless identification of cervical cancer. Machine Learning techniques are used by the system to extract features from the photographs and categorize them as normal or abnormal. The suggested method Naïve Bayes and Logistic Regression had an overall accuracy of 73 % after being trained and tested on a dataset of 1313 colposcopy images.