1. LiteCovidNet: A lightweight deep neural network model for detection of COVID‐19 using X‐ray images.
- Author
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Kumar, Sachin, Shastri, Sourabh, Mahajan, Shilpa, Singh, Kuljeet, Gupta, Surbhi, Rani, Rajneesh, Mohan, Neeraj, and Mansotra, Vibhakar
- Subjects
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ARTIFICIAL neural networks , *X-ray imaging , *COVID-19 , *MEDICAL personnel , *MEDICAL sciences , *DEEP learning - Abstract
The syndrome called COVID‐19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence‐based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network‐based "LiteCovidNet" model is proposed that detects COVID‐19 cases as the binary class (COVID‐19, Normal) and the multi‐class (COVID‐19, Normal, Pneumonia) bifurcated based on chest X‐ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi‐class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID‐19 infected patients at an early stage with convenient cost and better accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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