Application of Voting Based Approach on Deep Learning Algorithm for Lung Disease Classification

With the advent of the Deep learning era, an unprecedented change has come in the field of medical image analysis via CAD (Computer-Aided Diagnosis) [1]–[13] system. With feature extraction capability, Deep learning effectively performs the classification task and thus enhances the prediction of Medical images. CNN models like Dense net 121, Resnet-50, Alex net, Mobile Net, and Inceptionv3 have been used to identify lung disease, but no model was able to predict the particular class of illness with 100% accuracy. In this paper, we apply the voting method, which combines three models Dense net 121, Mobile Net, NasNet, and predictions are made on the basis of the majority of optimistic predictions. Our approach was found to improve the prediction of a particular class of lung disease like Bacterial Pneumonia, Covid, and Lung Opacity. Normal lungs are predicted with 100% accuracy with our approach.