Next Article in Journal
COTS-Based Architectural Framework for Reliable Real-Time Control Applications in Manufacturing
Previous Article in Journal
Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
Open AccessArticle

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

1
Department of Biomedical Physics & Technology, University of Dhaka, Dhaka 1000, Bangladesh
2
Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
3
Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka 1000, Bangladesh
4
Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh
5
Faculty of Robotics and Advanced Computing, Qatar Armed Forces-Academic Bridge Program, Qatar Foundation, Doha 24404, Qatar
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(9), 3233; https://doi.org/10.3390/app10093233
Received: 4 March 2020 / Revised: 31 March 2020 / Accepted: 8 April 2020 / Published: 6 May 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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. View Full-Text
Keywords: pneumonia; bacterial and viral pneumonia; chest X-ray; deep learning; transfer learning; image processing pneumonia; bacterial and viral pneumonia; chest X-ray; deep learning; transfer learning; image processing
Show Figures

Figure 1

MDPI and ACS Style

Rahman, T.; Chowdhury, M.E.H.; Khandakar, A.; Islam, K.R.; Islam, K.F.; Mahbub, Z.B.; Kadir, M.A.; Kashem, S. Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray. Appl. Sci. 2020, 10, 3233.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop