The Implementation of ConvNet technique for Automatic Detection of SARS-CoV-2


  • Salma Alhinai
  • Jitendra Pandey Middle East College


SARS-CoV-2, Deep Learning, Convolutional Neural Network, Chest x-ray


In the case of covid19 outbreak, most of the patients infected into their lungs started with pneumonia and throat infections with short of breathing. SARS-CoV-2 can negatively impact the lung, the gastrointestinal system, the brain, and the musculoskeletal system. It is basically can be a full body illness. The diagnosis of all such symptoms is possible with imaging technology like x-ray of chest which a radiologist can analyze to understand the severity of the infection. The researchers in Oman are trying to understand epidemiological trends to generate new Artificial Intelligence Algorithms to assist with covid19 disease detection, differentiation from other pneumonia and quantification of lung for therapy planning in advance. Predicting the SARS-CoV-2 disease in advance offers healthcare providers the opportunity to apply preventative measure that might improve patient safety, and quality of care, while lowering medical costs (Jarod Ferguson 2013). A group of researchers in some study concluded that the deep learning model showed comparable levels of performance with expert radiologists, and greatly improve the efficiency of radiologists in clinical practice (Elise Mak 2020). One type of deep learning, known as convolutional neural network, is particularly well suited to analyzing images, such as magnetic resonance imaging results or x-rays, it designed to operate more efficiently and handle larger images. Artificial Intelligence in medical imaging and diagnostics can conduct a comparative analysis of multiple x-ray scan images of the same patient and measure the changes in infections.


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How to Cite

Alhinai, S. ., & Pandey, J. (2022). The Implementation of ConvNet technique for Automatic Detection of SARS-CoV-2. Journal of Student Research. Retrieved from