An Efficient Ensemble-Based Deep Learning Model for the diagnosis of Cervical Cancer

Abstract

Biomedical imaging has shown us the blueprints of the interior parts of the human body for clinical research and medical support. Cervical Cancer (CC) is a common disease found mostly in females. Pap smear, also known as pap test, is largely adopted to diagnose cervical cancer. Early diagnosis of cervical cancer and proper treatment can help us to save millions of lives. In this piece of work, we have designed a unique algorithm to split the data uniformly and further proposed an ensemble based approach to diagnose cervical cancer. Fusion of multiple deep learning models helped us to achieve the same and boost up the efficiency of the proposed approach. Results revealed that the proposed algorithm shows 97% accuracy in performing classification activity, identifying cancerous and non-cancerous cells.