Diagnosis of COVID-19 and Viral Pneumonia with Chest X-Ray Images Using ResNet-34

COVID-19 is a highly contagious viral infection that primarily affects the respiratory system, causing symptoms such as high fever, cough, and severe respiratory distress. Early detection of the disease is of utmost importance to control the spread and severity. Common diagnostic methods include Reverse Transcription Polymerase Chain Reaction (RT-PCR), antigen tests, chest X-rays, and computed tomography (CT) scans. Similarly, viral pneumonia, another severe lung infection, leads to fluid or pus accumulation in the lungs, causing symptoms such as chest pain, fatigue, excessive sweating, and nausea. The elderly and young children are particularly vulnerable to severe complications. Similarly, viral pneumonia, another severe lung infection, leads to fluid or pus accumulation in the lungs, causing symptoms such as chest pain, fatigue, excessive sweating, and nausea. The elderly and young children are particularly vulnerable to severe complications. This paper aims to get the bottom of the ResNet-34 model, for detecting COVID-19 and Viral Pneumonia using radiography images and to build a web application using a Streamlit framework for detecting the presence of the disease. The ResNet-34 is a Convolutional Neural Network (CNN) model used to classify COVID-19, Viral Pneumonia, and normal (negative) among 15000 Chest X-ray images. In this work, datasets from Kaggle repository have considered that help to acquire Chest X-ray images as these are extensively used to diagnose COVID-19 and Viral Pneumonia as they give clear insights into the lungs. The presented resNet-34 model, helps to discriminate between COVID-19 and Viral Pneumonia with high accuracy and precision.