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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.<\/jats:p>","DOI":"10.1007\/s44163-024-00110-x","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:02:02Z","timestamp":1709683322000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A multiclass deep learning algorithm for healthy lung, Covid-19 and pneumonia disease detection from chest X-ray images"],"prefix":"10.1007","volume":"4","author":[{"given":"Geethu","family":"Mohan","sequence":"first","affiliation":[]},{"given":"M. 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