Densely attention mechanism based network for COVID-19 detection in chest X-rays

Article

Ullah, Zahid, Usman, Muhammad, Latif, Siddique and Gwak, Jeonghwan. 2023. "Densely attention mechanism based network for COVID-19 detection in chest X-rays." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-022-27266-9
Article Title

Densely attention mechanism based network for COVID-19 detection in chest X-rays

ERA Journal ID201487
Article CategoryArticle
AuthorsUllah, Zahid, Usman, Muhammad, Latif, Siddique and Gwak, Jeonghwan
Journal TitleScientific Reports
Journal Citation13 (1)
Article Number261
Number of Pages14
Year2023
PublisherNature Publishing Group
Place of PublicationUnited Kingdom
ISSN2045-2322
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-022-27266-9
Web Address (URL)https://www.nature.com/articles/s41598-022-27266-9
AbstractAutomatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
KeywordsCOVID‑19; chest X‑rays
ANZSRC Field of Research 20204299. Other health sciences
Byline AffiliationsKorea National University of Transportation, South Korea
Seoul National University, Korea
University of Southern Queensland
Permalink -

https://research.usq.edu.au/item/z2700/densely-attention-mechanism-based-network-for-covid-19-detection-in-chest-x-rays

Log in to edit

Download files

Published Version
s41598-022-27266-9.pdf
License: CC BY 4.0
File access level: Anyone

  • 45
    total views
  • 54
    total downloads
  • 4
    views this month
  • 3
    downloads this month

Export as

Related outputs

Medicine's New Rhythm: Harnessing Acoustic Sensing via the Internet of Audio Things for Healthcare
Pervez, Farrukh, Shoukat, Moazzam, Suresh, Varsha, Farooq, Muhammad Umar Bin, Sandhu, Moid, Qayyum, Adnan, Usama, Muhammad, Girardi, Adnan, Latif, Siddique and Qadir, Junaid. 2024. "Medicine's New Rhythm: Harnessing Acoustic Sensing via the Internet of Audio Things for Healthcare." IEEE Open Journal of the Computer Society. 5, pp. 491-510. https://doi.org/10.1109/OJCS.2024.3462812
SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation
Ullah, Zahid, Akram, Muhammad, Latif, Siddique, Khan, Asifullah and Gwak, Jeonghwan. 2023. "SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-023-36311-0
Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation
Rehman, Azka, Usman, Muhammad, Shahid, Abdullah, Latif, Siddique and Qadir, Junaid. 2023. "Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation." Sensors. 23 (4). https://doi.org/10.3390/s23042346
Groundwater Quality, Health Risk Assessment, and Source Distribution of Heavy Metals Contamination around Chromite Mines: Application of GIS, Sustainable Groundwater Management, Geostatistics, PCAMLR, and PMF Receptor Model
Rashid, Abdur, Ayub, Muhammad, Ullah, Zahid, Ali, Asmat, Sardar, Tariq, Iqbal, Javed, Gao, Xubo Gao, Bundschuh, Jochen, Li, Chengcheng, Khattak, Seema Anjum, Ali, Liaqat, El-Serehy, Hamed A., Kaushik, Prashant and Khan, Sardar. 2023. "Groundwater Quality, Health Risk Assessment, and Source Distribution of Heavy Metals Contamination around Chromite Mines: Application of GIS, Sustainable Groundwater Management, Geostatistics, PCAMLR, and PMF Receptor Model." International Journal of Environmental Research and Public Health. 20 (3). https://doi.org/10.3390/ijerph20032113
Geochemical control, water quality indexing, source distribution, and potential health risk of fluoride and arsenic in groundwater: Occurrence, sources apportionment, and positive matrix factorization model
Sarker, Abdur, Ayub, Muhammad, Bundschuh, Jochen, Gao, Xubo, Ullah, Zahid, Ali, Liaqat, Li, Chengcheng, Ahmad, Ajaz, Khan, Sardar, Rinklebe, Jörg and Ahmad, Parvaiz. 2023. "Geochemical control, water quality indexing, source distribution, and potential health risk of fluoride and arsenic in groundwater: Occurrence, sources apportionment, and positive matrix factorization model." Journal of Hazardous Materials. 460. https://doi.org/1016/j.jhazmat.2023.132443
Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Schuller, Bjorn W.. 2023. "Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition ." IEEE Transactions on Affective Computing. 14 (4), pp. 3164-3176. https://doi.org/10.1109/TAFFC.2022.3221749
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Schuller, Bjorn. 2023. "Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition." IEEE Transactions on Affective Computing. 14 (3), pp. 1912-1926. https://doi.org/10.1109/TAFFC.2022.3167013
A survey on deep reinforcement learning for audio‑based applications
Latif, Siddique, Cuayahuitl, Heriberto, Pervez, Farrukh, Shamshad, Fahad, Ali, Hafiz Shehbaz and Cambria, Erik. 2023. "A survey on deep reinforcement learning for audio‑based applications." Artificial Intelligence Review: an international survey and tutorial journal. 56 (3), p. 2193–2240. https://doi.org/10.1007/s10462-022-10224-2
Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja, Epps, Julien and Schuller, Bjorn W.. 2022. "Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition." IEEE Transactions on Affective Computing. 13 (2), pp. 992-1004. https://doi.org/10.1109/TAFFC.2020.2983669
Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing
Ali, Hafiz Shehbaz, Hassan, Fakhar ul, Latif, Siddique, Manzoor, Habib Ullah and Qadir, Junaid. 2021. "Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing." IEEE International Conference on Communications Workshops (2021). Montreal, Canada 14 - 23 Jun 2021 United States. https://doi.org/10.1109/ICCWorkshops50388.2021.9473669
Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation
Latif, Siddique, Kim, Inyoung, Calapodescu, Ioan and Besacier, Laurent. 2021. "Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation." 25th Conference on Computational Natural Language Learning (CoNLL 2021). Punta Cana, Dominican Republic 10 - 11 Nov 2021 Stroudsburg, Pennsylvania. https://doi.org/10.18653/v1/2021.conll-1.42
Deep Representation Learning for Speech Emotion Recognition
Latif, Siddique. 2022. Deep Representation Learning for Speech Emotion Recognition. PhD by Publication Doctor of Philosophy (DPHD). University of Southern Queensland. https://doi.org/10.26192/w8w00
Survey of Deep Representation Learning for Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja, Qadir, Junaid and Schuller, Bjorn. 2023. "Survey of Deep Representation Learning for Speech Emotion Recognition." IEEE Transactions on Affective Computing. 14 (2), pp. 1634-1654. https://doi.org/10.1109/TAFFC.2021.3114365
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Schuller, Bjorn W.. 2020. "Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition." 21st Annual Conference of the International Speech Communication Association: Cognitive Intelligence for Speech Processing (INTERSPEECH 2020). Shanghai, China 25 - 29 Oct 2020 France. https://doi.org/10.21437/Interspeech.2020-3190
Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Latif, Siddique, Asim, Muhammad, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Schuller, Bjorn W.. 2020. "Augmenting Generative Adversarial Networks for Speech Emotion Recognition." 21st Annual Conference of the International Speech Communication Association: Cognitive Intelligence for Speech Processing (INTERSPEECH 2020). Shanghai, China 25 - 29 Oct 2020 France. https://doi.org/10.21437/Interspeech.2020-3194
Federated Learning for Speech Emotion Recognition Applications
Latif, Siddique, Khalifa, Sara, Rana, Rajib and Jurdak, Raja. 2020. "Federated Learning for Speech Emotion Recognition Applications." 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2020). Sydney, Australia 21 - 24 Apr 2020 United States. https://doi.org/10.1109/IPSN48710.2020.00-16
Direct modelling of speech emotion from raw speech
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Epps, Julien. 2019. "Direct modelling of speech emotion from raw speech." 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language (INTERSPEECH 2019). Graz, Austria 15 - 19 Sep 2019 France. https://doi.org/10.21437/Interspeech.2019-3252
Variational Autoencoders to Learn Latent Representations of Speech Emotion
Latif, Siddique, Rana, Rajib, Qadir, Junaid and Epps, Julien. 2018. "Variational Autoencoders to Learn Latent Representations of Speech Emotion." 19th Annual Conference of the International Speech Communication Association: Speech Research for Emerging Markets in Multilingual Societies (INTERSPEECH 2018). Hyderabad, India 02 - 06 Sep 2018 France. https://doi.org/10.21437/Interspeech.2018-1568
Transfer learning for improving speech emotion classification accuracy
Latif, Siddique, Rana, Rajib, Younis, Shahzad, Qadir, Junaid and Epps, Julien. 2018. "Transfer learning for improving speech emotion classification accuracy." 19th Annual Conference of the International Speech Communication Association: Speech Research for Emerging Markets in Multilingual Societies (INTERSPEECH 2018). Hyderabad, India 02 - 06 Sep 2018 France. https://doi.org/10.21437/Interspeech.2018-1625
Automated screening for distress: A perspective for the future
Rana, Rajib, Latif, Siddique, Gururajan, Raj, Gray, Anthony, Mackenzie, Geraldine, Humphris, Gerald and Dunn, Jeff. 2019. "Automated screening for distress: A perspective for the future." European Journal of Cancer Care. 28 (4). https://doi.org/10.1111/ecc.13033
Phonocardiographic sensing using deep learning for abnormal heartbeat detection
Latif, Siddique, Usman, Muhammad, Rana, Rajib and Qadir, Junaid. 2018. "Phonocardiographic sensing using deep learning for abnormal heartbeat detection." IEEE Sensors Journal. 18 (22), pp. 9393-9400. https://doi.org/10.1109/JSEN.2018.2870759
Mobile health in the Developing World: review of literature and lessons from a case study
Latif, Siddique, Rana, Rajib, Qadir, Junaid, Ali, Anwaar, Imran, Muhammad Ali and Younis, Muhammad Shahzad. 2017. "Mobile health in the Developing World: review of literature and lessons from a case study." IEEE Access. 5, pp. 11540-11556. https://doi.org/10.1109/ACCESS.2017.2710800