Imperial College London
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ProfessorArnabMajumdar

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor of Transport Risk and Safety
 
 
 
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Contact

 

+44 (0)20 7594 6037a.majumdar

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Singh:2023:10.32604/cmc.2023.032364,
author = {Singh, S and Rawat, SS and Gupta, M and Tripathi, BK and Alanzi, F and Majumdar, A and Khuwuthyakorn, P and Thinnukool, O},
doi = {10.32604/cmc.2023.032364},
journal = {CMC-Computers Materials & Continua},
pages = {1673--1691},
title = {Deep attention network for pneumonia detection using chest X-ray images},
url = {http://dx.doi.org/10.32604/cmc.2023.032364},
volume = {74},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attention-aware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attention-aware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F- score of 0.92, indicating that the suggested model outperformed against the baseline models.
AU  - Singh,S
AU  - Rawat,SS
AU  - Gupta,M
AU  - Tripathi,BK
AU  - Alanzi,F
AU  - Majumdar,A
AU  - Khuwuthyakorn,P
AU  - Thinnukool,O
DO  - 10.32604/cmc.2023.032364
EP  - 1691
PY  - 2023///
SN  - 1546-2218
SP  - 1673
TI  - Deep attention network for pneumonia detection using chest X-ray images
T2  - CMC-Computers Materials & Continua
UR  - http://dx.doi.org/10.32604/cmc.2023.032364
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000886509600032&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.techscience.com/cmc/v74n1/49853
UR  - http://hdl.handle.net/10044/1/101228
VL  - 74
ER  -
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