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A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network

Nahid, Abdullah-Al, Sikder, Niloy, Bairagi, Anupam Kumar, Razzaque, Md. Abdur, Masud, Mehedi, Kouzani, Abbas Z. and Mahmud, M. A. Parvez 2020, A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network, Sensors, vol. 20, no. 12, pp. 1-18, doi: 10.3390/s20123482.

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Title A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network
Author(s) Nahid, Abdullah-Al
Sikder, Niloy
Bairagi, Anupam Kumar
Razzaque, Md. Abdur
Masud, Mehedi
Kouzani, Abbas Z.ORCID iD for Kouzani, Abbas Z. orcid.org/0000-0002-6292-1214
Mahmud, M. A. ParvezORCID iD for Mahmud, M. A. Parvez orcid.org/0000-0002-1905-6800
Journal name Sensors
Volume number 20
Issue number 12
Article ID 3482
Start page 1
End page 18
Total pages 18
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020-06
ISSN 1424-8220
Keyword(s) chest radiograph
deep learning
medical image processing
pneumonia
Summary Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.
Language eng
DOI 10.3390/s20123482
Indigenous content off
Field of Research 0301 Analytical Chemistry
0906 Electrical and Electronic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30139675

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.