TY - JOUR
T1 - Diagnosis of COVID-19 CT Scans Using Convolutional Neural Networks
AU - Chang, Victor
AU - Mcwann, Siddharth
AU - Hall, Karl
AU - Xu, Qianwen
AU - Ganatra, Meghana Ashok
PY - 2024/6/7
Y1 - 2024/6/7
N2 - Machine learning technology, particularly neural networks, provides useful tools for diagnosing diseases. This study focuses on how convolutional neural networks can be implemented to diagnose COVID-19 through the processing of x-ray images. This study demonstrates how the convolutional neural networks DenseNet201, ResNet152, VGG16, and InceptionV3 can aid healthcare providers in the diagnosis of COVID-19. The models returned accuracies of 98.73%, 97.23%, 91.25% and 98.38% respectively. The results from these experiments are compared to previous studies by evaluating F1-score, accuracy, precision and recall. Additionally, the important problems of hyperparameter tuning and data imbalance are explored and addressed. Areas for future research in this area are also suggested.
AB - Machine learning technology, particularly neural networks, provides useful tools for diagnosing diseases. This study focuses on how convolutional neural networks can be implemented to diagnose COVID-19 through the processing of x-ray images. This study demonstrates how the convolutional neural networks DenseNet201, ResNet152, VGG16, and InceptionV3 can aid healthcare providers in the diagnosis of COVID-19. The models returned accuracies of 98.73%, 97.23%, 91.25% and 98.38% respectively. The results from these experiments are compared to previous studies by evaluating F1-score, accuracy, precision and recall. Additionally, the important problems of hyperparameter tuning and data imbalance are explored and addressed. Areas for future research in this area are also suggested.
U2 - 10.1007/s42979-024-02878-2
DO - 10.1007/s42979-024-02878-2
M3 - Article
SN - 2662-995X
VL - 5
JO - SN Computer Science
JF - SN Computer Science
IS - 5
M1 - 625
ER -