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Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

Sukhendra Singh1, Sur Singh Rawat2, Manoj Gupta3, B. K. Tripathi4, Faisal Alanzi5, Arnab Majumdar6, Pattaraporn Khuwuthyakorn7, Orawit Thinnukool7,*

1 Department of Information Technology, JSS Academy of Technical Education, Noida, India
2 JSS Academy of Technical Education, Noida, India
3 Department of Electronics and Communication Engineering, JECRC University Jaipur, Rajasthan, India
4 Harcourt Butler Technological University Kanpur, India
5 Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, College of Engineering, Al Kharj, 16278, Saudi Arabia
6 Faculty of Engineering, Imperial College London, London, SW7 2AZ, UK
7 College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

* Corresponding Author: Orawit Thinnukool. Email: email

Computers, Materials & Continua 2023, 74(1), 1673-1691.


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.


Cite This Article

S. Singh, S. S. Rawat, M. Gupta, B. K. Tripathi, F. Alanzi et al., "Deep attention network for pneumonia detection using chest x-ray images," Computers, Materials & Continua, vol. 74, no.1, pp. 1673–1691, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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