An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass...

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Bibliographic Details
Published in:Journal of signal processing systems Vol. 95; no. 2-3; pp. 101 - 113
Main Authors: Teodoro, Arthur A. M., Silva, Douglas H., Saadi, Muhammad, Okey, Ogobuchi D., Rosa, Renata L., Otaibi, Sattam Al, Rodríguez, Demóstenes Z.
Format: Journal Article
Language:English
Published: New York Springer US 2023
Springer Nature B.V
Subjects:
Algorithms
Artificial neural networks
Circuits and Systems
Classification
Computed tomography
Computer Imaging
Coronaviruses
COVID-19
Electrical Engineering
Engineering
Feature extraction
Image Processing and Computer Vision
Impact analysis
Kernel functions
Machine learning
Medical imaging
Pattern Recognition
Pattern Recognition and Graphics
Polymerase chain reaction
Respiratory diseases
Severe acute respiratory syndrome coronavirus 2
Signal,Image and Speech Processing
Support vector machines
Viral diseases
Vision
COVID-19; non-COVID-19
Convolutional neural networks
Computed tomography images
Transfer learning
Machine learning
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Summary:The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.
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ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-021-01714-7