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Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Australasian physical & engineering sciences in medicine, 2020-06, Vol.43 (2), p.635-640 [Peer Reviewed Journal]

Australasian College of Physical Scientists and Engineers in Medicine 2020. ;ISSN: 0158-9938 ;EISSN: 2662-4737 ;EISSN: 1879-5447 ;DOI: 10.1007/s13246-020-00865-4 ;PMID: 32524445

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Title:
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
Author: Apostolopoulos, Ioannis D ; Mpesiana, Tzani A
Subjects: Abnormalities ; Artificial neural networks ; Bacteria ; Betacoronavirus ; Biomarkers ; Computer architecture ; Coronavirus Infections - diagnostic imaging ; Coronaviruses ; COVID-19 ; Databases, Factual ; Datasets ; Deep Learning ; Diagnostic systems ; Failure rates ; Humans ; Image classification ; Image detection ; Machine learning ; Medical imaging ; Medical research ; Neural networks ; Neural Networks, Computer ; Pandemics ; Performance evaluation ; Pneumonia ; Pneumonia, Bacterial - diagnostic imaging ; Pneumonia, Viral - diagnostic imaging ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiography, Thoracic ; SARS-CoV-2 ; X ray imagery ; X-rays
Is Part Of: Australasian physical & engineering sciences in medicine, 2020-06, Vol.43 (2), p.635-640
Description: In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.
Publisher: Switzerland: Springer Nature B.V
Language: English
Identifier: ISSN: 0158-9938
EISSN: 2662-4737
EISSN: 1879-5447
DOI: 10.1007/s13246-020-00865-4
PMID: 32524445
Source: MEDLINE
ProQuest Central

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Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Australasian College of Physical Scientists and Engineers in Medicine 2020. ;ISSN: 0158-9938 ;EISSN: 2662-4737 ;EISSN: 1879-5447 ;DOI: 10.1007/s13246-020-00865-4 ;PMID: 32524445

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