Paper Title
REGIONS PREDICTION AND DETECTION IN COLPOSCOPY IMAGE USING MACHINE LEARNING FOR CLINICAL AID

Abstract
Colposcopy is one of the gold-standard medical procedures for examining any signs of abnormality or precancerous conditions. This procedure uses a colposcope device that captures precancerous cells digitally. These digital images are vital for clinicians to identify any abnormality. However, accurately identifying these abnormalities can be challenging and time-consuming even for experienced medical professionals. Therefore, there is a requirement for an automated system that predict the regions from colposcopy images. This study employed a machine learning-based approach, a random forest model, which segments and predict the region in colposcopy images. The random forest model achieved good accuracy of 0.92, precision of 0.90, and f1 score of 0.95. Keywords - Colposcopy, Random Forest Model. Segmentation.