Abstract
COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.
Highlights
In the current decade, medical facilities are increasing day by day for the betterment of human beings
Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with noninfected people
Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19-related symptoms
Summary
Medical facilities are increasing day by day for the betterment of human beings. COVID-19 is declared a pandemic disease by the World Health Organization (WHO) in March 2020 after the different countries’ infected and death rate data (Karim et al, 2020). The detection of the presence of the COVID-19 virus is a time-consuming task as it is done currently in clinical laboratories. (v) Integrating the optimizer algorithm, activation function, and loss function with Binary classification using CNN has further improved the detection rate of COVID-19 cases using the CT images, and the findings obtained were analyzed. The proposed technique can provide the detection of COVID -19 in patients in real-time, and the time taken for the process is less than what the lab tests are taking in the present scenario. The challenges of present work and conclusion along with future prespective has been discussed
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