A Framework for Detecting Cervical Cancer Based on UD-MHDC Segmentation and MBD-RCNN Classification Techniques

Abstract

Cervical cancer is a cancer of the entrance to the uterus. Pap test has made cervical cancer quite possibly one of the most preventable types of cancer, which can be utilized for its initial identification. Be that as it may, the whole interaction is tedious, expensive and involves observer biases. In order to conquer existing challenges the work has developed an automated cervical cancer detection framework using the UDMHDC segmentation and MBD-RCNN classification algorithm. The proposed segmentation technique and MBDRCNN model provides accurate classification of cervical cancer along with low computational time.