Art

J-GLOBAL ID：202102211265365587   Reference number：21A0615608

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

胸部X線を用いた肺炎検出のための深層畳込みニューラルネットワーク(CNN)による転移学習【JST・京大機械翻訳】

Author (8)： Rahman Tawsifur (Department of Biomedical Physics & Technology, University of Dhaka, Dhaka 1000, Bangladesh) ,  Chowdhury Muhammad E. H. (Department of Electrical Engineering, Qatar University, Doha 2713, Qatar) ,  Khandakar Amith (Department of Electrical Engineering, Qatar University, Doha 2713, Qatar) ,  Islam Khandaker R. (Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka 1000, Bangladesh) ,  Islam Khandaker F. (Department of Electrical Engineering, Qatar University, Doha 2713, Qatar) ,  Mahbub Zaid B. (Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh) ,  Kadir Muhammad A. (Department of Biomedical Physics & Technology, University of Dhaka, Dhaka 1000, Bangladesh) ,  Kashem Saad (Faculty of Robotics and Advanced Computing, Qatar Armed Forces-Academic Bridge Program, Qatar Foundation, Doha 24404, Qatar)
Material： Applied Sciences (Web)
Volume： 10  Issue： 9  Page： 3233  Publication year： 2020 
JST Material Number： U7135A  ISSN： 2076-3417  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： Pneumonia is a life-threatenin...

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Thesaurus term： *learning ,  airport ,  pretreatment ,  bacterium ,  viral infection ,  *pneumonia ,  digital method ,  X-ray image ,  screening(inspection) ,  early diagnosis ,  viral pneumonia
Semi thesaurus term： *胸部X線撮影 ,  分類法 ,  分類精度 ,  *転移学習 , 【Automatic Indexing@JST】

Author keywords (6)： pneumonia ,  bacterial and viral pneumonia ,  chest X-ray ,  deep learning ,  transfer learning ,  image processing

JST classification (2)： Respiratory system diagnosis  (GI02000S) ,  Medical image processing  (JE04020T)

Reference (53)：

• Imran, A. Training a CNN to detect Pneumonia. 2019. Available online: https://medium.com/datadriveninvestor/training-a-cnn-to-detect-pneumonia-c42a44101deb (accessed on 23 December 2019).
• Aydogdu, M.; Ozyilmaz, E.; Aksoy, H.; Gursel, G.; Ekim, N. Mortality prediction in community-acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores. Tuberk Toraks 2010, 58, 25-34.
• WHO. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children; World Health Organization: Geneva, Switzerland, 2001.
• Neuman, M.I.; Lee, E.Y.; Bixby, S.; DiPerna, S.; Hellinger, J.; Markowitz, R.; Servaes, S.; Monuteaux, M.C.; Shah, S. Variability in the interpretation of chest radiographs for the diagnosis of pneumonia in children. J. Hosp. Med. 2011, 7, 294-298.
• Dele Davies, H.; Wang, E.E.-L.; Manson, D.; Babyn, P.S.; Shuckett, B. Reliability of the chest radiograph in the diagnosis of lower respiratory infections in young children. Pediatr. Infect. Dis. J. 1996, 15, 600-604.

Terms in the title (5)： 胸部X線 ,  肺炎 ,  深層畳込みニューラルネットワーク ,  CNN ,  転移学習