2026-05-19T05:53:21.334Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:9193 2022-12-01T21:45:01Z 2

Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/9193 https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-32/issue-02/021406/Intelligent-diagnosis-of-coronavirus-with-computed-tomography-images-using-a/10.1117/1.JEI.32.2.021406.full M. Sarac M. Mravik D. Jovanovic I. Strumberger M. Zivkovic N. Bacanin 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. article 10.1117/1.JEI.32.2.021406 32 2, 021406 1 10 1017-9909 JOURNAL OF ELECTRONIC IMAGING