Selected article for: "diagnostic performance and sensitivity diagnostic performance"
Author: Kavuran, Gürkan; İn, Erdal; Geçkil, Ayşegül Altıntop; Şahin, Mahmut; Berber, Nurcan Kırıcı
Title: MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net
  • Cord-id: mb93w4v1
  • Document date: 2021_9_27
    • ID: mb93w4v1
    Snippet: PURPOSE: The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT). MATERIALS AND METHODS: In this retrospective study, a novel hybrid model (MTU-COVNet) was developed to extract visual features from volumetric thoracic CT scans for the detection of COVID-19. The collected dataset consisted of 3210 CT scans from 953 patients. Of the total 3210 scans in the final dataset, 1327 (41%) were obtaine
    Document: PURPOSE: The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT). MATERIALS AND METHODS: In this retrospective study, a novel hybrid model (MTU-COVNet) was developed to extract visual features from volumetric thoracic CT scans for the detection of COVID-19. The collected dataset consisted of 3210 CT scans from 953 patients. Of the total 3210 scans in the final dataset, 1327 (41%) were obtained from the COVID-19 group, 929 (29%) from the CAP group, and 954 (30%) from the Normal CT group. Diagnostic performance was assessed with the area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity. RESULTS: The proposed approach with the optimized features from concatenated layers reached an overall accuracy of 97.7% for the CT-MTU dataset. The rest of the total performance metrics, such as; specificity, sensitivity, precision, F1 score, and Matthew Correlation Coefficient were 98.8%, 97.6%, 97.8%, 97.7%, and 96.5%, respectively. This model showed high diagnostic performance in detecting COVID-19 pneumonia (specificity: 98.0% and sensitivity: 98.2%) and CAP (specificity: 99.1% and sensitivity: 97.1%). The areas under the ROC curves for COVID-19 and CAP were 0.997 and 0.996, respectively. CONCLUSION: A deep learning–based AI system built on the CT imaging can detect COVID-19 pneumonia with high diagnostic efficiency and distinguish it from CAP and normal CT. AI applications can have beneficial effects in the fight against COVID-19.

    Search related documents:
    Co phrase search for related documents
    • accuracy improvement and acute respiratory syndrome coronavirus: 1, 2, 3, 4, 5, 6, 7
    • accuracy improvement and lung disease: 1
    • accuracy performance and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • accuracy performance and acute respiratory syndrome coronavirus: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
    • accuracy performance and lung disease: 1, 2, 3, 4
    • accuracy result and acute respiratory syndrome: 1, 2, 3, 4, 5
    • accuracy result and acute respiratory syndrome coronavirus: 1, 2, 3, 4
    • accuracy result and lung disease: 1, 2, 3
    • accuracy value and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
    • accuracy value and acute respiratory syndrome coronavirus: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • accuracy value and lung disease: 1, 2, 3, 4
    • acute respiratory syndrome and admission center: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • acute respiratory syndrome and local patient: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
    • acute respiratory syndrome and low respiratory tract: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • acute respiratory syndrome and lung disease: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute respiratory syndrome coronavirus and admission center: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • acute respiratory syndrome coronavirus and local patient: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • acute respiratory syndrome coronavirus and low respiratory tract: 1, 2, 3, 4, 5
    • acute respiratory syndrome coronavirus and lung disease: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25