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Updated: Jul 6, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer
R Betshrine Rachel1, H Khanna Nehemiah1, Vaibhav Kumar Singh2
1Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India.
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%.
