Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

• Published in: Medical & biological engineering & computing Vol. 59; no. 4; pp. 825 - 839
• Main Authors: Singh, Mukul, Bansal, Shrey, Ahuja, Sakshi, Dubey, Rahul Kumar, Panigrahi, Bijaya Ketan, Dey, Nilanjan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021; Springer Nature B.V
• Subjects: Area Under Curve; Artificial neural networks; Automation; Bagging; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification; Classifiers; Computed tomography; Computer Applications; Containment; Coronaviridae; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - virology; Datasets as Topic; Deep learning; Feature extraction; Human Physiology; Humans; Imaging; Learning algorithms; Lung - diagnostic imaging; Machine Learning; Medical imaging; Medical personnel; Model testing; Motivation; Neural networks; Original; Original Article; Pandemics; Principal components analysis; Radiology; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Support Vector Machine; Support vector machines; Tomography, X-Ray Computed - methods; Transfer learning; Viral diseases; COVID-19; CT scan data; Ensemble SVM; Transfer learning; VGG16
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-020-02299-2

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning–based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning–based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.