Abstract
Since computer vision has been a very emerging and happening approach in image categorization, this article describes how chest X-ray images of diverse infected and normal samples were classified using convolution neural networks, mostly under the following five categories: normal or no lung infection, COVID-19, SARS, ARDS and other pneumonia infections such as viral pneumonia, cavitating pneumonia, streptococcus pneumonia, legionella pneumonia and pneumocystis pneumonia. The proposed approach accepts the X-ray image inputs and diagnoses the lung infection under the aforementioned five categories of major pneumonia infections. Pre-trained models like VGG19, Resnet-50, and NasNetMobile are applied to the diagnosis of chest X-ray images, and their performance is compared against a newly proposed convolutional neural network called the COVNET 2020, since the network is inspired to identify the COVID-19 chest X-ray images. Transfer learning with pre-rained neural networks on massive image databases like “imagenet” has become de facto for medical image diagnosis.
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The data used to support the findings of this study are available from the corresponding author upon request.
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Raghavendran, P.S., Ragul, S., Asokan, R. et al. A new method for chest X-ray images categorization using transfer learning and CovidNet_2020 employing convolution neural network. Soft Comput 27, 14241–14251 (2023). https://doi.org/10.1007/s00500-023-08874-7
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DOI: https://doi.org/10.1007/s00500-023-08874-7