TY - GEN
T1 - COVID-Net Architecture Modification for Covid-19 Detection on Chest X-ray Images
AU - Wahyuningrum, Rima Tri
AU - Wahyudi, Moh Imam
AU - Angkoso, Cucun Very
AU - Satoto, Budi Dwi
AU - Sari, Amillia Kartika
AU - Sensusiati, Anggraini Dwi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The coronavirus-based illness known as Covid-19 is easily spread. Because Covid-19 spread quickly and easily, many people were exposed to it. As a result, finding a quick and accurate way to diagnose Covid-19 is necessary, and using chest X-ray pictures is one such option. The best way to describe the health of the lungs in Covid-19 patients is through X-ray scans. The Kaggle website provided the dataset for this study. The 4345 photos in this collection are divided into three classes: 1500 Covid-19 images, 1500 normal images, and 1345 pneumonia images. The Convolutional Neural Network (CNN), which employs the modified COVID-Net architecture, is the technique suggested in the Covid-19 detection procedure. Therefore, an architecture known as COVID-Net was created specifically for diagnosing Covid-19. The COVID-Net architecture uses epoch 10, batch size 32, and a learning rate of 0.0001 with kernel sizes ranging from 7x7 to 1x1. As a result, Covid-19 could be identified from chest X-ray images by COVID-Net. Based on the findings of tests performed using 5-fold Cross Validation, an average F1-score accuracy value of 93% and an AUC value of 98.78% were obtained. Based on the F1 score and AUC values, it can be said that the modified COVID-Net architecture is a useful model.
AB - The coronavirus-based illness known as Covid-19 is easily spread. Because Covid-19 spread quickly and easily, many people were exposed to it. As a result, finding a quick and accurate way to diagnose Covid-19 is necessary, and using chest X-ray pictures is one such option. The best way to describe the health of the lungs in Covid-19 patients is through X-ray scans. The Kaggle website provided the dataset for this study. The 4345 photos in this collection are divided into three classes: 1500 Covid-19 images, 1500 normal images, and 1345 pneumonia images. The Convolutional Neural Network (CNN), which employs the modified COVID-Net architecture, is the technique suggested in the Covid-19 detection procedure. Therefore, an architecture known as COVID-Net was created specifically for diagnosing Covid-19. The COVID-Net architecture uses epoch 10, batch size 32, and a learning rate of 0.0001 with kernel sizes ranging from 7x7 to 1x1. As a result, Covid-19 could be identified from chest X-ray images by COVID-Net. Based on the findings of tests performed using 5-fold Cross Validation, an average F1-score accuracy value of 93% and an AUC value of 98.78% were obtained. Based on the F1 score and AUC values, it can be said that the modified COVID-Net architecture is a useful model.
KW - COVID-Net
KW - Convolutional Neural Network (CNN)
KW - Covid-19
KW - chest x-ray image
KW - classification
UR - http://www.scopus.com/inward/record.url?scp=85161357653&partnerID=8YFLogxK
U2 - 10.1109/ONCON56984.2022.10126769
DO - 10.1109/ONCON56984.2022.10126769
M3 - Conference contribution
AN - SCOPUS:85161357653
T3 - 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
BT - 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
Y2 - 9 December 2022 through 11 December 2022
ER -