Source of Publication
Computer Systems Science and Engineering
The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia).
Computers, Materials and Continua (Tech Science Press)
Computer Sciences | Medicine and Health Sciences
Classification, Convolutional neural network, Deep learning, X-ray
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abugabah, Ahed; Mehmood, Atif; Al Zubi, Ahmad Ali; and Sanzogni, Louis, "Smart COVID-3D-SCNN: A novel method to classify x-ray images of COVID-19" (2022). All Works. 4656.
Indexed in Scopus
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series