COVID-19 DETECTION FROM CHEST X-RAY IMAGES USING DEEP LEARNING

Autor: MOHAMMAD TAREK AZIZ, JUEL SIKDER, TAOHIDUR RAHMAN, ARMANDO DACALCAP DEL MUNDO, S.M FAHIM FAISAL, NAYEEM UDDIN AHMED KHAN
Jazyk: angličtina
Rok vydání: 2022
Předmět:
DOI: 10.5281/zenodo.7330742
Popis: At present, COVID-19 has become a severe threat to students, teachers, doctors, scientists, and governments all over the world. It is a single-stranded RNA virus with one of the enormous RNA genomes, and it is changing through mutation in every day. Sometimes this mutation results in a new variant. According to medical research of COVID-19 infected patients, these individuals are most commonly infected with a lung illness after coming into touch with the virus. So, find out COVID-19 from a chest X-ray image is an appropriate technique. But another issue arises when it shows that other diseases like viral pneumonia, and lung opacity also had common symptoms like as COVID-19 and these problems also can be detected from chest X-ray images. So, in this research, we proposed a deep learning approach based on modified VGG-16 for detecting COVID-19, viral pneumonia, lung-Opacity, and normal chest. We used the COVID-19 Radiography dataset to evaluate the performance of the proposed system. The accuracy of classification using the proposed method is 92.28%.
Databáze: OpenAIRE