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The Journal of Supercomputing

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29-09-2023

A pyramid GNN model for CXR-based COVID-19 classification

Authors: Chang Jie, Chen Jiming, Shao Ying, Tong Yanchun, Ren Haodong

Published in: The Journal of Supercomputing | Issue 4/2024

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Abstract

The urgent need for efficient COVID-19 diagnosis has spurred advancements in chest X-ray (CXR) radiography, particularly with the aid of deep learning technologies like convolutional neural networks (CNNs) and graph neural networks (GNNs). Yet, the scarcity of labeled CXR images due to privacy constraints and the complexity of COVID-19 phenotypes often hamper model performance. In this study, we present an innovative pyramid GNN model that effectively tackles these challenges. By segmenting a CXR image into patches, our model leverages a CNN to capture shallow features, then employs a pyramid graph structure within GNN layers to gain the inter-relationship of infected region in distant patches and to amalgamate high-level features. These are subsequently processed by a multi-layer perceptron classifier for final diagnosis. Our approach offers multiple benefits, including noise elimination without the need for pre-treatment, efficient examination of remote infection regions, and the ability to accommodate the intricate structure of the lungs. Evaluations conducted on three distinct public CXR image datasets suggest that our pyramid GNN model offers a promising pathway for enhancing the accuracy and efficiency of COVID-19 diagnosis.

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Hopkins J et al (2020) Coronavirus resource center. Im Internet (Stand: 19.04.2020). https://coronavirus.jhu.edu/data

Leung T, Chan A, Chan E, Chan V, Chui C, Cowling B, Gao L, Ge M, Hung I, Ip M et al (2020) Short-and potential long-term adverse health outcomes of COVID-19: a rapid review. Emerg Microbes Infect 9(1):2190–2199CrossRefPubMedPubMedCentral

Manna S, Wruble J, Maron SZ, Toussie D, Voutsinas N, Finkelstein M, Cedillo MA, Diamond J, Eber C, Jacobi A et al (2020) COVID-19: a multimodality review of radiologic techniques, clinical utility, and imaging features. Radiol Cardiothorac Imaging 2(3):e200210CrossRefPubMedPubMedCentral

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31

Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 24(3):1207–1220CrossRefPubMedPubMedCentral

Goel T, Murugan R, Mirjalili S, Chakrabartty DK (2022) Multi-COVID-Net: multi-objective optimized network for COVID-19 diagnosis from chest X-ray images. Appl Soft Comput 115:108250CrossRefPubMed

Tharwat A, Houssein EH, Ahmed MM, Hassanien AE, Gabel T (2018) Mogoa algorithm for constrained and unconstrained multi-objective optimization problems. Appl Intell 48(8):2268–2283CrossRef

10.

Fitriasari HI, Rizkinia M (2021) Improvement of Xception-ResNet50V2 concatenation for COVID-19 detection on chest X-ray images. In: 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT). IEEE, pp 343–347

11.

Karacı A (2022) VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm. Neural Comput Appl 34(10):8253–8274CrossRefPubMedPubMedCentral

12.

Al-Antari MA, Hua C-H, Bang J, Lee S (2021) Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest X-ray images. Appl Intell 51(5):2890–2907CrossRef

13.

Chola C, Mallikarjuna P, Muaad AY, Bibal Benifa J, Hanumanthappa J, Al-antari MA (2021) A hybrid deep learning approach for COVID-19 diagnosis via CT and X-ray medical images. In: Computer Sciences & Mathematics Forum, vol 2. MDPI, p 13

14.

Ukwuoma CC, Qin Z, Heyat MBB, Akhtar F, Bamisile O, Muaad AY, Addo D, Al-Antari MA (2023) A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res 48:191–211CrossRefPubMed

15.

Ukwuoma CC, Qin Z, Heyat MBB, Akhtar F, Smahi A, Jackson JK, Furqan Qadri S, Muaad AY, Monday HN, Nneji GU (2022) Automated lung-related pneumonia and COVID-19 detection based on novel feature extraction framework and vision transformer approaches using chest X-ray images. Bioengineering 9(11):709CrossRefPubMedPubMedCentral

16.

LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef

17.

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25

18.

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

19.

Tan M, Le, Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. PMLR, pp 6105–6114

20.

Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28

21.

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431–3440

22.

Hemdan EED, Shouman MA, Karar ME (2020) Covidx-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv preprint arXiv:2003.11055

23.

Nigam B, Nigam A, Jain R, Dodia S, Arora N, Annappa B (2021) COVID-19: automatic detection from X-ray images by utilizing deep learning methods. Expert Syst Appl 176:114883CrossRefPubMedPubMedCentral

24.

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792CrossRefPubMedPubMedCentral

25.

Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788

26.

Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C, Damaševičius R, De Albuquerque VHC (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10(2):559CrossRef

27.

Xia X, Togneri R, Sohel F, Huang D (2018) Auxiliary classifier generative adversarial network with soft labels in imbalanced acoustic event detection. IEEE Trans Multimed 21(6):1359–1371CrossRef

28.

Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR (2020) Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection. IEEE Access 8:91916–91923CrossRefPubMed

29.

Haritha D, Praneeth C, Pranathi MK (2020) Covid prediction from X-ray images. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS). IEEE, pp 1–5

30.

Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640CrossRefPubMedPubMedCentral

31.

Jyoti K, Sushma S, Yadav S, Kumar P, Pachori RB, Mukherjee S (2023) Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images. Comput Biol Med 152:106331CrossRefPubMed

32.

Chamseddine E, Mansouri N, Soui M, Abed M (2022) Handling class imbalance in COVID-19 chest X-ray images classification: using smote and weighted loss. Appl Soft Comput 129:109588CrossRefPubMedPubMedCentral

33.

Barshooi AH, Amirkhani A (2022) A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-ray images. Biomed Signal Process Control 72:103326CrossRefPubMed

34.

Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest X-ray images. Sci Rep 10(1):1–12

35.

Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) Covid-caps: a capsule network-based framework for identification of covid-19 cases from X-ray images. Pattern Recogn Lett 138:638–643CrossRefADS

36.

Montalbo FJP (2021) Diagnosing covid-19 chest X-rays with a lightweight truncated densenet with partial layer freezing and feature fusion. Biomed Signal Process Control 68:102583CrossRefPubMedPubMedCentral

37.

Feki I, Ammar S, Kessentini Y, Muhammad K (2021) Federated learning for COVID-19 screening from chest X-ray images. Appl Soft Comput 106:107330CrossRefPubMedPubMedCentral

38.

Bhattacharya A, Gawali M, Seth J, Kulkarni V (2022) Application of federated learning in building a robust COVID-19 chest X-ray classification model. arXiv preprint arXiv:2204.10505

39.

Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y et al (2022) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45(1):87–110CrossRefPubMed

40.

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16 \(\times\) 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

41.

Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision. Springer, pp 213–229

42.

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012–10022

43.

Zhang L, Wen Y (2021) A transformer-based framework for automatic COVID19 diagnosis in chest CTs. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 513–518

44.

Zhang L, Wen Y (2021) MIA-COV19D: a transformer-based framework for COVID19 classification in chest CTs. arXiv

45.

Anwar T (2021) COVID19 diagnosis using AutoML from 3D CT scans. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 503–507 (2021)

46.

Krishnan KS, Krishnan KS (2021) Vision transformer based COVID-19 detection using chest X-rays. In: 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, pp 644–648

47.

Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903

48.

Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

49.

Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: methods and applications. arXiv preprint arXiv:1709.05584

50.

Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp 1263–1272

51.

Li X-S, Liu X, Lu L, Hua X-S, Chi Y, Xia K (2022) Multiphysical graph neural network (MP-GNN) for COVID-19 drug design. Brief Bioinform 23(4):231CrossRef

52.

Cheung M, Moura JM (2020) Graph neural networks for COVID-19 drug discovery. In: 2020 IEEE International Conference on Big Data (Big Data). IEEE, pp 5646–5648

53.

Wang L, Ben X, Adiga A, Sadilek A, Tendulkar A, Venkatramanan S, Vullikanti A, Aggarwal G, Talekar A, Chen J et al (2020) Using mobility data to understand and forecast covid19 dynamics. medRxiv

54.

Xie H, Li D, Wang Y, Kawai Y (2022) Visualization method for the spreading curve of COVID-19 in universities using GNN. In: 2022 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, pp 121–128

55.

Yu Z, Zheng X, Yang Z, Lu B, Li X, Fu M (2021) Interaction-temporal GCN: a hybrid deep framework for COVID-19 pandemic analysis. IEEE Open J Eng Med Biol 2:97–103CrossRefPubMed

56.

Song X, Li H, Gao W, Chen Y, Wang T, Ma G, Lei B (2021) Augmented multicenter graph convolutional network for COVID-19 diagnosis. IEEE Trans Ind Inf 17(9):6499–6509CrossRef

57.

Lu S, Zhu Z, Gorriz JM, Wang S-H, Zhang Y-D (2022) NAGNN: classification of COVID-19 based on neighboring aware representation from deep graph neural network. Int J Intell Syst 37(2):1572–1598CrossRef

58.

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

59.

Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. arXiv preprint arXiv:2003.11597

60.

Chowdhury ME, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Al Emadi N et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665–132676CrossRef

61.

Lisa M, Bot H. My research software. https://doi.org/10.5281/zenodo.1234. https://github.com/github/linguist

62.

Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30 (2017)

Title: A pyramid GNN model for CXR-based COVID-19 classification
Authors: Chang Jie
Chen Jiming
Shao Ying
Tong Yanchun
Ren Haodong
Publication date: 29-09-2023
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 4/2024
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-023-05633-1

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