Deep Learning for Covid-19 Identification: A Comparative Analysis
International Research Journal on Advanced Science Hub,
2022, Volume 4, Issue 11, Pages 272-280
AbstractCovid 19 was an epidemic in 2022. Detection of Covid in X-Ray samples is crucial for diagnosis and treatment. This was also challenging for the identification of covid by radiologists. This study proposes Transfer Learning for detecting Covid-19 from X-Ray images. The proposed Transfer Learning detects the normal x-ray and covid 19 x-ray samples. In addition to this proposed model, different architectures including trained Desnet121, Efficient B4, Resnet 34, and mobilenetv2 were evaluated for the covid dataset. Our suggested model has compared the existing covid-19 detection algorithm in terms of accuracy. The Experimental model detects covid 19 patients with an accuracy of 98 percent. Our proposed work is to analyse the covid19 by the automation with helps of deep learning algorithms which results in high accuracy in detection Covid19 using x-ray samples. This model can assist radiologists and doctors in the diagnosis of covid-19 and make the test more accessible.
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