A Deep Learning Based Approach for Efficient Diagnoses of COVID-19, Viral Illnesses (Other than Covid-19), and Bacterial Illnesses via Chest X-Rays
According to the World Health Organization, the coronavirus has killed more than 2.7 million people worldwide. Efficiently and accurately diagnosing people with COVID-19 is essential to help slow down the spread of the virus. Although swab tests do exist, they are not easily accessible in rural areas, whereas Chest X-Ray scanning has been available before the pandemic. However there is a lack of radiologists to analyze and diagnose illnesses from Chest X-Rays. This is why this research aims to use deep learning for efficient diagnoses of COVID-19, Viral (Other than COVID-19), and Bacterial illnesses via Chest X-Ray images. Since the deep learning models had to analyze images, a CNN (Convolutional Neural Network) was built. There were three different CNN architectures fine-tuned and applied on real-time patient data. The VGG-16 fine-tuned CNN model received the highest testing accuracy of 92.34% This project idea was presented to Congresswoman Bonamici and two doctors. Further improvements will be made to this project.