TY  - JOUR
AB  - In 2020, the world faced an unprecedented pandemic outbreak of cor-onavirus disease (COVID-19), which causes severe threats to patients suffering from diabetes, kidney problems, and heart problems. A rapid testing mechanism is a primary obstacle to controlling the spread of COVID-19. Current tests focus on the reverse transcription-polymerase chain reaction (RT-PCR). The PCR test takes around 4?6 h to identify COVID-19 patients. Various research has recom-mended AI-based models leveraging machine learning, deep learning, and neural networks to classify COVID-19 and non-COVID patients from chest X-ray and computerized tomography (CT) scan images. However, no model can be claimed as a standard since models use different datasets. Convolutional neural network (CNN)-based deep learning models are widely used for image analysis to diag-nose and classify various diseases. In this research, we develop a CNN-based diagnostic model to detect COVID-19 patients by analyzing the features in CT scan images. This research considered a publicly available CT scan dataset and fed it into the proposed CNN model to classify COVID-19 infected patients. The model achieved 99.76%, 96.10%, and 96% accuracy in training, validation, and test phases, respectively. It achieved scores of 0.986 in area under curve (AUC) and 0.99 in the precision-recall curve (PRC). We compared the model?s performance to that of three state-of-the-art pretrained models (MobileNetV2, InceptionV3, and Xception). The results show that the model can be used as a diagnostic tool for digital healthcare, particularly in COVID-19 chest CT image classification.
AU  - Masud, M
AU  - Alshehri, MD
AU  - Alroobaea, R
AU  - Shorfuzzaman, M
DA  - 2021/01/01
DO  - 10.32604/iasc.2021.016800
EP  - 13
JO  - Intelligent Automation and Soft Computing
PB  - TECH SCIENCE PRESS
PY  - 2021/01/01
SP  - 1
TI  - Leveraging convolutional neural network for COVID-19 disease detection using CT scan images
VL  - 29
Y1  - 2021/01/01
Y2  - 2026/05/12
ER  -