Affiliations: [a] Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India
| [b] Alumna, Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India
| [c] Alumna, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India
Correspondence:
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Corresponding author: H. Khanna Nehemiah, Professor, Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai 600025, Tamil Nadu, India. Tel.: +91 93810 38339; E-mail: [email protected].
Abstract: 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%.
