Implementation of Transfer Learning for Covid-19 and Pneumonia Disease Detection Through Chest X-Rays Based on Web

Abstract

Coronavirus disease 2019, known as COVID-19, attacks the human respiratory system caused by severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2). COVID-19 disease and pneumonia show similar symptoms such as fever, cough, even headache. Diagnosis of pneumonia can be tested through diagnostic tests, including blood tests, chest X-rays, and pulse oximetry, while the diagnosis of COVID-19 recommended by WHO is with swab test (RT-PCR). But in fact, the swab test method takes a relatively long time, for about one to seven days, for the result, and is not cheap. For that, there needs to be a development that can be one of the options in diagnosing COVID-19 and pneumonia at once, especially since both diseases have similar symptoms. One option that can be done is the diagnosis using a chest X-ray. This research aims to detect COVID-19 disease and pneumonia through chest X-rays using transfer learning to increase the accuracy of disease diagnosis with a more efficient time. The architecture used is EfficientNet B0 with variations in optimization parameters, learning rates, and epochs. EfficientNet B0 Adam optimization with a learning rate of 0.001 in the 6th epochs is a great model that we obtained. Furthermore, the evaluation of the model got accuracy, precision, recall, and f1-score of 92%. Then the model visualization is done using Grad-CAM. To implement the best model, web application development is done to make it easier to detect COVID-19 disease and pneumonia. Keywords : COVID-19; pneumonia; EfficientNet; transfer learning; web