MSENet: Mean and standard deviation based ensemble network for cervical cancer detection

Cervical cancer is one of the most concerning carcinogenic diseases among women worldwide. The condition is especially bad in low- or middle-income countries due to the lack of medical facilities. In such situations, computer-aided diagnosis (CAD) systems can alleviate the need to a large extent. However, sometimes a single learning model may not be effective enough to capture relevant information for accurate prediction of diseases from complex data. To this end, we propose an ensemble of deep learning models, called Mean and Standard Deviation-based Ensemble Network (MSENet), for detecting cervical cancer from Pap smear images. Our ensemble model consists of three standard base classifiers, namely Xception, Inception V3, and VGG-16. We further improve the classification abilities of these models by implementing a novel probability enhancement scheme that takes into account the mean and standard deviation of the confidence scores. This technique enables the overall framework to capture complementary information offered by the base classifiers and is tailored to the characteristics of deep learners. Finally, we use the product rule to aggregate the obtained outcomes and get final predictions. The proposed MSENet has been evaluated on a standard public benchmark dataset, called SIPaKMeD. With a classification accuracy of 97.21% using a 5-fold cross-validation scheme, the MSENet outperforms many state-of-the-art methods. The source codes are made public in the following Github repository https://github.com/rishavpramanik/msenet.