Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model

• Main Authors: Bacanin, N. (Author), Jovanovic, D. (Author), Mravik, M. (Author), Sarac, M. (Author), Strumberger, I. (Author), Zivkovic, M. (Author)
• Format: Article
• Language: English
• Published: SPIE 2023
• Subjects: Computed tomography images; Computed tomography lung image; computed tomography lung images; Computerized tomography; coronavirus; Coronavirus; Coronaviruses; COVID-19; deep learning; Deep learning; detail extraction pyramid network; Detail extraction pyramid network; Details extractions; Diagnosis; Extraction; Intelligent diagnosis; Learning models; Learning systems; Medical computing; Pulmonary diseases; Pyramid network; Respiratory system; severe acute respiratory syndrome; Severe acute respiratory syndrome
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 10179909 (ISSN)
• DOI: 10.1117/1.JEI.32.2.021406

The coronavirus (COVID-19) disease appeared as a respiratory system disorder and has triggered pneumonia outbreaks globally. As this COVID-19 disease drastically spread around the world, computed tomography (CT) has helped to diagnose it rapidly. It is imperative to implement a faultless computer-aided model for detecting COVID-19-affected patients through CT images. Therefore, a detail extraction pyramid network (DEPNet) is proposed to predict COVID-19-affected cases from CT images of the COVID-CT-MD dataset. In this study, the COVID-CT-MD dataset is applied to detect the accuracy of the deep learning technique; the dataset has CT scans of 169 patients; among those, 60 patients are COVID-19 positive patients, and 76 cases are normal. These affected patients were clinically verified with the standard hospital. The deep learning-oriented CT diagnosis model is implemented to detect COVID-19-affected patients. The experiment revealed that the proposed model categorized COVID-19 cases from other respiratory-oriented diseases with 99.45% accuracy. Further, this model selected the exact lesion parts, mainly ground-glass opacity, which helped the doctors to diagnose visually. © 2022 SPIE and IS&T.