A multiclass deep learning algorithm for healthy lung, Covid-19 and pneumonia disease detection from chest X-ray images

Abstract A crucial step in the battle against the coronavirus disease 2019 (Covid-19) pandemic is efficient screening of the Covid affected patients. Deep learning models are used to improve the manual judgements made by healthcare professionals in classifying Chest X-Ray (CXR) images into Covid pneumonia, other viral/bacterial pneumonia, and normal images. This work uses two open source CXR image dataset having a total of 15,153 (dataset 1), and 4575 (dataset 2) images respectively. We trained three neural network models with a balanced subset of dataset 1 (1345 images per class), balanced dataset 2 (1525 images per class), and an unbalanced full dataset 1. The models used are VGG16 and Inception Resnet (IR) using transfer learning and a tailor made Convolutional Neural Network (CNN). The first model, VGG16 gives an accuracy, sensitivity, specificity, and F1 score of 96%, 97.8%, 95.92%, 97% respectively. The second model, IR gives an accuracy, sensitivity, specificity and F1 score of 97%, 98.51%, 97.28%, 99% respectively. The third and best proposed model, CNN gives an accuracy, sensitivity, specificity, and F1 score of 97%, 98.21%, 96.62%, 98% respectively. These performance metrics were obtained for the balanced dataset 1 and all models used 80:10:10 cross validation technique. The highest accuracy using CNN for all the three datasets are 97%, 96%, and 93% respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) is used to ensure that the model uses genuine pathology markers to generalize.