Diagnosis of COVID-19 from computed tomography slices using flower pollination algorithm, k-nearest neighbor, and support vector machine classifiers

Coronavirus disease 19 (COVID-19), caused by the severe acute respiratory syndrome-coronavirus-2 virus, is commonly diagnosed through imaging techniques such as computed tomography (CT) scans, which reveal characteristic lung lesions. In this study, we propose a computer-aided diagnosis (CAD) system to assist in the early detection of COVID-19 from CT lung slices, leveraging advanced machine-learning algorithms for precise and efficient analysis. To achieve this, we developed a CAD system that diagnoses COVID-19 from CT lung slices. An adaptive Wiener filter was applied to remove noise from the CT images. The chest tissues were then segmented using an optimal thresholding method to extract regions of interest, which represent the COVID-19 lesions under investigation. The feature vectors were divided into training and testing with an 80/20 ratio. A wrapper-based flower pollination algorithm was employed alongside the k-nearest neighbor classifier to select the optimal feature set. These selected features were subsequently used to train a support vector machine (SVM) classifier. With feature selection, the SVM achieved an accuracy of 91.30% on a real-time dataset, outperforming seven other machine learning classifiers (radial basis function-SVM, k nearest neighbor, linear discriminant analysis, random forest, naïve Bayes, AdaBoost, extreme gradient boosting) and four deep learning classifiers (convolutional neural network, recurrent neural network, long short term memory, Bidirectional long short term memory). For the publicly available COVID-19 CT dataset, an accuracy of 88.18% was achieved. In conclusion, our COVID-19 CAD system improves diagnostic accuracy, with future work aimed at enhancing efficiency and expanding to covariant detection and severity assessment.