COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images.

Mahmoud Ragab,Samah Alshehri,Jaber Alyami,Hibah M Aldawsari,S Abdel-Khalek,Adeeb Noor,Gamil Abdel Azim

doi:10.3389/fpubh.2022.819156

Abstract

Diagnosis is a crucial precautionary step in research studies of the coronavirus disease, which shows indications similar to those of various pneumonia types. The COVID-19 pandemic has caused a significant outbreak in more than 150 nations and has significantly affected the wellness and lives of many individuals globally. Particularly, discovering the patients infected with COVID-19 early and providing them with treatment is an important way of fighting the pandemic. Radiography and radiology could be the fastest techniques for recognizing infected individuals. Artificial intelligence strategies have the potential to overcome this difficulty. Particularly, transfer learning MobileNetV2 is a convolutional neural network architecture that can perform well on mobile devices. In this study, we used MobileNetV2 with transfer learning and augmentation data techniques as a classifier to recognize the coronavirus disease. Two datasets were used: the first consisted of 309 chest X-ray images (102 with COVID-19 and 207 were normal), and the second consisted of 516 chest X-ray images (102 with COVID-19 and 414 were normal). We assessed the model based on its sensitivity rate, specificity rate, confusion matrix, and F1-measure. Additionally, we present a receiver operating characteristic curve. The numerical simulation reveals that the model accuracy is 95.8% and 100% at dropouts of 0.3 and 0.4, respectively. The model was implemented using Keras and Python programming.

Highlights

The coronavirus disease (COVID-19) has endangered social lives and created disturbing financial costs
We present a COVID-19 X-ray classification technique based on transfer learning with Mobile NetV2, present in the appendix, which is a pre-trained design
The main contribution of this paper is to investigate and evaluate the performance of MobileNetv2 as a lightweight convolutional neural with transfer-learning and augmentation data for the early detection of coronavirus, to propose and adopt a new model structure for MobileNetv2 for binary classification, and to investigate the COVIDComputer-aided diagnostic (CCAD) tool based on the MobilenetV2 developed

Summary

Introduction

The coronavirus disease (COVID-19) has endangered social lives and created disturbing financial costs. Many studies have attempted to find a way to manage its spread and the resulting death. Many research propositions have been made to evaluate the existence and seriousness of pneumonia triggered by COVID-19 [1–4]. Compared to CT, radiography is accessible in hospitals worldwide, X-ray images are considered less delicate for examining patients with COVID19. Primary analysis is crucial for instant seclusion of the infected individuals. It decreases the rate of infection in a healthy population because of the accessibility of sufficient therapy or vaccination for the virus

Methods

Results

Conclusion