Abstract: Coronavirus is a large virus family consisting of diverse viruses, some of
which disseminate among mammals and others cause sickness among humans.
COVID-19 is highly contagious and is rapidly spreading, rendering its early
diagnosis of preeminent status. Researchers, medical specialists and
organizations all over the globe have been working tirelessly to combat this
virus and help in its containment. In this paper, a novel neural network called
WisdomNet has been proposed, for the diagnosis of COVID-19 using chest X-rays.
The WisdomNet uses the concept of Wisdom of Crowds as its founding idea. It is
a two-layered convolutional Neural Network (CNN), which takes chest x-ray
images as input. Both layers of the proposed neural network consist of a number
of neural networks each. The dataset used for this study consists of chest
x-ray images of COVID-19 positive patients, compiled and shared by Dr. Cohen on
GitHub, and the chest x-ray images of healthy lungs and lungs affected by viral
and bacterial pneumonia were obtained from Kaggle. The network not only
pinpoints the presence of COVID-19, but also gives the probability of the
disease maturing into Acute Respiratory Distress Syndrome (ARDS). Thus,
predicting the progression of the disease in the COVID-19 positive patients.
The network also slender the occurrences of false negative cases by employing a
high threshold value, thus aids in curbing the spread of the disease and gives
an accuracy of 100% for successfully predicting COVID-19 among the chest x-rays
of patients affected with COVID-19, bacterial and viral pneumonia.