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CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images by Gaurav Srivastava; Aninditaa Chauhan; Mahesh Jangid; Sandeep Chaurasia is a Medicine article available to read on EtoBox.

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The Coronavirus (COVID-19) pandemic has created havoc on humanity by causing millions of deaths and adverse physical and mental health effects. To prepare humankind for the fast and efficient detection of the virus and its variants shortly, COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. To detect COVID-19, there are numerous publicly accessible datasets of Chest X-rays that the researchers have combined to solve the problem of inadequate data. The cause for concern here is that in combining two or more datasets, some of the images might be duplicates, so a curated dataset has been used in this study, taken from an author's paper. This dataset consists of 1281 COVID-19, 3270 Normal X-rays, and 1656 viral-pneumonia infected Chest X-ray images. Dataset has been pre-processed and divided carefully to ensure that there are no duplicate images. A comparative study on many traditional pre-trained models was performed, analyzing top-performing models. Fine-tuned InceptionV3, Modified EfficientNet B0&B1 produced an accuracy of 99.78% on binary classification, i.e., covid-19 infected and normal Chest X-ray image. ResNetV

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Author

Gaurav Srivastava; Aninditaa Chauhan; Mahesh Jangid; Sandeep Chaurasia

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Medicine (Physical Sciences)