International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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COVID-19 Diagnosis from CXR images through pre-trained Deep Visual Embeddings

  • Khalid, Shahzaib (Department of Telecommunication Engineering Mehran University of Engineering & Technology) ;
  • Syed, Muhammad Shehram Shah (Department of Software Engineering Mehran University of Engineering & Technology) ;
  • Saba, Erum (Information Technology Centre Sindh Agriculture University) ;
  • Pirzada, Nasrullah (Department of Telecommunication Engineering Mehran University of Engineering & Technology)
  • 투고 : 2022.05.05
  • 발행 : 2022.05.30
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초록

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

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참고문헌

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