An improved ensemble deep belief model (EDBM) for pap-smear cell image classification

Applications of deep learning models for medical image analysis have been concentrated in recent years. This study presents an improved automatic Deep Learning Ensemble model to detect cervical cancer. The proposed model takes advantage of the Deep Belief Network and Ensemble classification model called EDBM to improve the accuracy of the prediction. In this method, a new classification layer using Belief Network with Dempster combinational rule combines the evidence to handle uncertainty. An ensemble classifier was applied to address the issue of sample rejection in previous classification models. This method used three classifiers, including Support Vector Machine, K-Nearest Neighbour, and Fuzzy classifier, to improve the model's performance. Experimental results on the Herlev dataset and the SIPaKMeD dataset revealed the superiority of the proposed EDBM model. The EDBM On the Herlev dataset with an accuracy of 99%, specificity of 98%, the sensitivity of 98%, and AUC of 99.98%, and the SIPaKMeD dataset with an accuracy of 97.2%, specificity of 98.79%, the sensitivity of 98.70%, and AUC of 99.89%, outperformed counterpart Deep learning methods and showed promising achievements in the early detection of Cervical Cancer.