TY  - EJOU
AU  - Basalamah, Anas 
AU  - Rahman, Shadikur 

TI  - An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images
T2  - Computer Systems Science and Engineering

PY  - 2022
VL  - 40
IS  - 1
SN  - 

AB  - This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19 (Negative or Positive). Then, 2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models. Among those, the optimized model architecture classifier technique achieves higher accuracy (0.97) than four other models, specifically VGG-16, VGG-19, RestNet18, and RestNet50 (0.96, 0.72, 0.91, and 0.93, respectively). Therefore, this study will enable radiologists to more efficiently and effectively classify a patient’s coronavirus disease.
KW  - X-ray image classification; X-ray feature extraction; COVID-19; coronavirus disease; convolutional neural networks; optimized model

DO  - 10.32604/csse.2022.016949