Selected article for: "accuracy training and machine learning deep learning"
Author: Rezaeijo, Seyed Masoud; Ghorvei, Mohammadreza; Abedi-Firouzjah, Razzagh; Mojtahedi, Hesam; Entezari Zarch, Hossein
Title: Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms
  • Cord-id: mcjay3x6
  • Document date: 2021_6_11
    • ID: mcjay3x6
    Snippet: BACKGROUND: This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. RESULTS: The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the
    Document: BACKGROUND: This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. RESULTS: The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. CONCLUSIONS: The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

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