Detection of Pneumonia and COVID-19 Based on X-ray using Transfer Learning

Abstract

COVID-19, also referred to as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), is a highly contagious disease that is transmitted through respiratory droplets containing saliva or mucus. The virus can spread rapidly through close contact with infected individuals or contaminated surfaces. Pneumonia, another infectious illness, is often caused by bacterial infection in the alveoli of the lungs, leading to inflammation and pus buildup. Accurate diagnosis of these diseases is crucial for effective treatment and prevention of fatalities. Deep Learning techniques have emerged as a promising approach to aid medical experts in diagnosing patients with these diseases. Specifically, Convolutional Neural Networks (CNN) have been applied to predict and detect the presence of COVID-19 or pneumonia using chest X-ray images with high accuracy and efficiency.

In this research, we developed VGG16 convolutional neural network (CNN) architecture which is developed by the Visual Geometry Group at the University of Oxford. It is a deep learning model that is widely used for image classification tasks. The study utilized a dataset collected by The researchers of Qatar University have compiled the COVID-QU-Ex dataset, which consists of 33,920 chest X-ray (CXR) images including: 11,956 COVID-19, 11,263 Non-COVID infections (Viral or Bacterial Pneumonia), and 10,701 Normal, the research study can detect and predict COVID-19, bacterial, and viral-pneumonia diseases based on chest X-ray images.