TY - GEN
T1 - SARS CovidAID
T2 - 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023
AU - Farjana, Afia
AU - Tabassum Liza, Fatema
AU - Al Mamun, Miraz
AU - Das, Madhab Chandra
AU - Maruf Hasan, Md
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The COVID-19 outbreak has presented significant challenges to medical professionals worldwide and underscored the need for accurate and effective detection methods due to its highly contagious nature and potential for explosive community transmission. However, healthcare delivery has been hindered by a lack of testing kits. To address this, deep learning techniques have been utilized to diagnose COVID-19 using CT scans, which have higher sensitivity in detecting early pneumonic changes. However, limited access to large datasets of CT-scan images due to privacy concerns has made developing accurate models difficult. To overcome this, transfer-learning pre-trained models have been employed in this study to automatically detect COVID-19 cases from CT scan images. The proposed methodology utilizes VGG19, RESNet50, and DenseNet169 architectures to classify patients as COVID-19 (positive) or COVID-19 (negative), with DenseNet169 performing the best with an accuracy of 98.5% in predicting COVID-19 binary classification. The model showed no signs of overfitting or underfitting, with a great output curve relative to the training accuracy. The other models, ResNet-50 and VGG-19 showed performance well with an accuracy of 96.7% and 92.7%, respectively. However, VGG-19 had the lowest accuracy of 92.7%. The findings of this study demonstrate the potential of using machine learning methods for the accurate and timely prediction of COVID-19. DenseNet169 outperformed other models and provided better accuracy for the prediction of COVID-19 Binary Classification.
AB - The COVID-19 outbreak has presented significant challenges to medical professionals worldwide and underscored the need for accurate and effective detection methods due to its highly contagious nature and potential for explosive community transmission. However, healthcare delivery has been hindered by a lack of testing kits. To address this, deep learning techniques have been utilized to diagnose COVID-19 using CT scans, which have higher sensitivity in detecting early pneumonic changes. However, limited access to large datasets of CT-scan images due to privacy concerns has made developing accurate models difficult. To overcome this, transfer-learning pre-trained models have been employed in this study to automatically detect COVID-19 cases from CT scan images. The proposed methodology utilizes VGG19, RESNet50, and DenseNet169 architectures to classify patients as COVID-19 (positive) or COVID-19 (negative), with DenseNet169 performing the best with an accuracy of 98.5% in predicting COVID-19 binary classification. The model showed no signs of overfitting or underfitting, with a great output curve relative to the training accuracy. The other models, ResNet-50 and VGG-19 showed performance well with an accuracy of 96.7% and 92.7%, respectively. However, VGG-19 had the lowest accuracy of 92.7%. The findings of this study demonstrate the potential of using machine learning methods for the accurate and timely prediction of COVID-19. DenseNet169 outperformed other models and provided better accuracy for the prediction of COVID-19 Binary Classification.
KW - Covid-19
KW - Deep Learning
KW - DenseNet169
KW - Machine Learning Technique
KW - SARS CoV-19
KW - classification algorithms
UR - http://www.scopus.com/inward/record.url?scp=85170641116&partnerID=8YFLogxK
U2 - 10.1109/SmartNets58706.2023.10216235
DO - 10.1109/SmartNets58706.2023.10216235
M3 - Conference Proceeding
AN - SCOPUS:85170641116
T3 - 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023
BT - 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 July 2023 through 27 July 2023
ER -