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Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray
Applied sciences, 2020-05, Vol.10 (9), p.3233
[Peer Reviewed Journal]
COPYRIGHT 2020 MDPI AG ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app10093233
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Title:
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray
Author:
Rahman, Tawsifur
;
Chowdhury, Muhammad E.H
;
Khandakar, Amith
;
Islam, Khandaker R
;
Islam, Khandaker F
;
Mahbub, Zaid B
;
Kadir, Muhammad A
;
Kashem, Saad
Subjects:
Artificial neural networks
;
bacterial and viral pneumonia
;
Care and treatment
;
chest X-ray
;
Chest x-rays
;
deep learning
;
Diagnosis
;
image processing
;
Pneumonia
;
transfer learning
Is Part Of:
Applied sciences, 2020-05, Vol.10 (9), p.3233
Description:
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Publisher:
MDPI AG
Language:
English
Identifier:
ISSN: 2076-3417
EISSN: 2076-3417
DOI: 10.3390/app10093233
Source:
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals
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