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Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images (2210.16584v2)

Published 29 Oct 2022 in eess.IV, cs.CV, and cs.LG

Abstract: Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.

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References (59)
  1. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine, 43(2):635–640, 2020.
  2. Choquet integral and coalition game-based ensemble of deep learning models for covid-19 screening from chest x-ray images. IEEE Journal of Biomedical and Health Informatics, 25(12):4328–4339, 2021.
  3. Can ai help in screening viral and covid-19 pneumonia? IEEE Access, 8:132665–132676, 2020.
  4. Who declares covid-19 a pandemic. Acta Bio Medica: Atenei Parmensis, 91(1):157, 2020.
  5. Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860, 2019.
  6. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp.  248–255. Ieee, 2009.
  7. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  8. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  9. A fuzzy fine-tuned model for covid-19 diagnosis. Computers in Biology and Medicine, pp.  106483, 2023.
  10. An optimized deep learning architecture for the diagnosis of covid-19 disease based on gravitational search optimization. Applied Soft Computing, 98:106742, 2021.
  11. Multiscale vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  6824–6835, 2021.
  12. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  3146–3154, 2019.
  13. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.
  14. Beyond self-attention: External attention using two linear layers for visual tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  15. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  770–778, 2016.
  16. Improving the performance of cnn to predict the likelihood of covid-19 using chest x-ray images with preprocessing algorithms. International journal of medical informatics, 144:104284, 2020.
  17. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
  18. Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation. In 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp.  16–23. IEEE, 2017.
  19. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  7132–7141, 2018.
  20. Ccnet: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pp.  603–612, 2019.
  21. A fuzzy-enhanced deep learning approach for early detection of covid-19 pneumonia from portable chest x-ray images. Neurocomputing, 481:202–215, 2022.
  22. A combined deep cnn-lstm network for the detection of novel coronavirus (covid-19) using x-ray images. Informatics in medicine unlocked, 20:100412, 2020.
  23. Portable chest x-ray in coronavirus disease-19 (covid-19): A pictorial review. Clinical imaging, 64:35–42, 2020.
  24. Learning distinctive filters for covid-19 detection from chest x-ray using shuffled residual cnn. Applied Soft Computing, 99:106744, 2021.
  25. Coronet: A deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Computer methods and programs in biomedicine, 196:105581, 2020.
  26. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  27. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012.
  28. Sars-net: Covid-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recognition, 122:108255, 2022.
  29. The incubation period of coronavirus disease 2019 (covid-19) from publicly reported confirmed cases: estimation and application. Annals of internal medicine, 172(9):577–582, 2020.
  30. Deep learning and medical image analysis for covid-19 diagnosis and prediction. Annual Review of Biomedical Engineering, 24, 2022a.
  31. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pp.  10012–10022, 2021.
  32. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  11976–11986, 2022b.
  33. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.
  34. Multi-task driven explainable diagnosis of covid-19 using chest x-ray images. Pattern Recognition, 122:108243, 2022.
  35. Bagan: Data augmentation with balancing gan. arXiv preprint arXiv:1803.09655, 2018.
  36. Recurrent models of visual attention. Advances in neural information processing systems, 27, 2014.
  37. xvitcos: explainable vision transformer based covid-19 screening using radiography. IEEE Journal of Translational Engineering in Health and Medicine, 10:1–10, 2021.
  38. Fuzzy enhancement and deep hash layer based neural network to detect covid-19. Journal of Intelligent & Fuzzy Systems, (Preprint):1–11, 2021.
  39. Andrew Ng et al. Sparse autoencoder. CS294A Lecture notes, 72(2011):1–19, 2011.
  40. Intensive care management of coronavirus disease 2019 (covid-19): challenges and recommendations. The lancet respiratory medicine, 8(5):506–517, 2020.
  41. Designing network design spaces. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  10428–10436, 2020.
  42. Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images. Computers in biology and medicine, 132:104319, 2021.
  43. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pp.  618–626, 2017.
  44. Global self-attention networks for image recognition. arXiv preprint arXiv:2010.03019, 2020.
  45. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  46. Mfbcnnc: Momentum factor biogeography convolutional neural network for covid-19 detection via chest x-ray images. Knowledge-Based Systems, 232:107494, 2021.
  47. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, volume 31, 2017.
  48. Going deeper with image transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  32–42, 2021.
  49. Covinet: A convolutional neural network approach for predicting covid-19 from chest x-ray images. Journal of Ambient Intelligence and Humanized Computing, 13(1):535–547, 2022.
  50. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  51. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1):1–12, 2020.
  52. Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  7794–7803, 2018.
  53. Frequency and distribution of chest radiographic findings in patients positive for covid-19. Radiology, 296(2):E72–E78, 2020.
  54. Pale transformer: A general vision transformer backbone with pale-shaped attention. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp.  2731–2739, 2022.
  55. Ulnet for the detection of coronavirus (covid-19) from chest x-ray images. Computers in Biology and Medicine, 137:104834, 2021.
  56. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  1492–1500, 2017.
  57. Ocnet: Object context for semantic segmentation. International Journal of Computer Vision, 129(8):2375–2398, 2021.
  58. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.
  59. Deepvit: Towards deeper vision transformer. arXiv preprint arXiv:2103.11886, 2021.
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