Selected article for: "accuracy specificity sensitivity and lung classification"
Author: Aboul Ella Hassanien; Lamia Nabil Mahdy; Kadry Ali Ezzat; Haytham H. Elmousalami; Hassan Aboul Ella
Title: Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine
  • Document date: 2020_4_6
    • ID: 45dpoepu_30
    Snippet: This study examined the performance of classification models for identification COVID-19. The experimental studies were implemented using the MATLAB 2019a deep learning toolbox. The results were obtained using a laptop equipped with an Intel Core i7, 18 GB of RAM and an AMD Radeon GPU. MATLAB was used to execute all the graphics and visualization functions. This model reads the data from a graphics file in the MATLAB workspace. Then multi-level t.....
    Document: This study examined the performance of classification models for identification COVID-19. The experimental studies were implemented using the MATLAB 2019a deep learning toolbox. The results were obtained using a laptop equipped with an Intel Core i7, 18 GB of RAM and an AMD Radeon GPU. MATLAB was used to execute all the graphics and visualization functions. This model reads the data from a graphics file in the MATLAB workspace. Then multi-level thresholding was conducted to reduce number of objects in lung image the supported vector machine was applied to classify infected lung with COVID-19. Figure 4 (a-c) shows the image of normal case with normal lungs, Multi-level effects on the normal lung image and the final normal lung image depends on SVM respectively. Figure 4 (d-f) shows the image of COVID-19 case with infected lungs, Multi-level effects on the infected lung image and the final infected lung image depend on SVM, respectively. Sensitivity, specificity, accuracy, for the proposed system. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76%, 99.7%, and 97.48%, respectively as illustrated in Table. 1. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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