Chest CT Images Analysis with Deep-Learning and Handcrafted Based Algorithms for COVID-19 Diagnosis

  • Juman Faris Abdulkareem, Hadeel K. Aljobouri, Ali M. Hasan
Keywords: ANOVA feature selection, Convolutional Neural Network, COVID-19, Deep Learning, Handcrafted features, GLCM, Hybrid features.

Abstract

After the outbreak of coronavirus disease 2019 (COVID-19), researchers have been working to develop fast and reliable diagnostic methods. The Computerized Tomography (CT) scan has shown to be highly effective in the diagnosis of COVID-19. Meanwhile, Computer-Aided Diagnosis (CAD) systems in terms of deep learning algorithms have progressed significantly. As a result, deep learning models for automatic COVID-19 detection from CT images were proposed. This study proposed Deep-learning, handcrafted, and Hybrid models for classification of axial lung CT-scans into two groups (COVID-19 and NonCOVID-19). Convolutional Neural Network (CNN) and Gray Level Co-occurrence Matrix (GLCM) act to extract Deep learning and Handcrafted features respectively, also a hybrid features (CNN and GLCM ) model was constructed to combine the effect of two models. For classification a dense multi-layer classifier was introduced. Finally to further enhance model performance a feature selection algorithm was employed. For training and testing of the models a Dataset collected for Iraqi patients from Ibn Al-Nafis teaching hospital was collected. The results obtained approved that the combined features extracted significantly improved the classification performance, with maximum classification accuracy achieved by the CNN+GLCM (with ANOVA feature selection) of 99.3% for the collected dataset.

Published
2021-09-05
How to Cite
Ali M. Hasan, J. F. A. H. K. A. (2021). Chest CT Images Analysis with Deep-Learning and Handcrafted Based Algorithms for COVID-19 Diagnosis . Design Engineering, 6246-6262. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/4059
Section
Articles