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Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

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  • معلومة اضافية
    • المصدر:
      Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9000080 Publication Model: Print Cited Medium: Internet ISSN: 1095-9114 (Electronic) Linking ISSN: 08953996 NLM ISO Abbreviation: J Xray Sci Technol Subsets: MEDLINE
    • بيانات النشر:
      Publication: 1997- : Amsterdam : IOS Press
      Original Publication: San Diego [i.e. Duluth, MN] : Academic Press, [c1989-
    • الموضوع:
      Support Vector Machine* ; COVID-19*/diagnostic imaging; Humans ; Animals ; Whales ; SARS-CoV-2 ; Algorithms ; Neural Networks, Computer ; Tomography, X-Ray Computed ; COVID-19 Testing
    • نبذة مختصرة :
      Background: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
      Objective: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented.
      Methods: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features.
      Results: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered.
      Conclusion: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.
    • Contributed Indexing:
      Keywords: Covid-19; MLP; SVM; WOA; kendall’s correlation coefficient graph
    • الموضوع:
      Date Created: 20240108 Date Completed: 20240401 Latest Revision: 20240401
    • الموضوع:
      20240401
    • الرقم المعرف:
      10.3233/XST-230196
    • الرقم المعرف:
      38189732