Deep Learning for Covid-19 Identification: A Comparative Analysis

Abstract

Covid-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 DenseNet121, EfficientNet 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 analyze the covid-19 by automation with the help of deep learning algorithms which results in high accuracy in detecting Covid-19 using x-ray samples. This model can assist radiologists and doctors in the diagnosis of covid-19 and make the test more accessible.