Browsing by Author "Benierbah, Said"

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    Five deep dearning models for classification of chest X-ray images of COVID-19
    (University of Oum El Bouaghi, 2021) Ferdi, Abdesselam; Benierbah, Said; Ferdi, Youcef
    In recent years, deep learning algorithms have acquired a great popularity in many fields including medical imaging to achieve different tasks such as image segmenta-tion, classification, detection, and retrieval tasks. Although reverse transcription po-lymerase chain reaction test (RT-PCR) is considered to be the gold standard for CO-VID- 19 screening, deep learning algorithms may help radiologists to automatically diagnose this disease by processing chest Xray (CXR) and chest CT images. This paper proposes a comparative study of five deep learning models for classification of CXR COVID-19 images. The five selected models are pre-trained networks, namely, ResNet-50, InceptionResNet-v2, Inception-v3, MobileNet-v2, and GoogleNet. The models were evaluated using CXR COVID-19 and normal images from publicly avail-able datasets. The obtained classification results show that the GoogleNet model achieves the highest classification performance with a validation accuracy of 95.69%.