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Diagnosis of Pneumonia from Chest X-ray Images with Vision Transformer Approach

Year 2024, , 324 - 334, 29.06.2024
https://doi.org/10.54287/gujsa.1464311

Abstract

People can get pneumonia, a dangerous infectious disease, at any time in their lives. Severe cases of pneumonia can be fatal. A doctor would usually examine chest x-rays to diagnose pneumonia. In this work, a pneumonia diagnosis system was developed using publicly available chest x-ray images. Vision Transformer (ViT) and other deep learning models were used to extract features from these images. Vision Transformer (ViT) is an attention-based model used for image processing and understanding as an alternative to the convolutional neural networks traditionally used for this purpose. ViT consists of a series of attention layers, where each attention layer models the relationships between input pixels to represent an image. These relationships are determined by a set of attention heads and then fed into a classifier. ViT performs effectively in a variety of visual tasks, especially when trained on large datasets. The study shows that the ViT model's classification procedure has a high success rate of 95.67%. These results highlight how deep learning models can be used to quickly and accurately diagnose dangerous diseases such as pneumonia in its early stages. The study also shows that the ViT model outperforms current approaches in the biomedical field.

References

  • Akter, S., Shamsuzzaman, & Jahan, F. (2015). Community Acquired Pneumonia. International Journal of Respiratory and Pulmonary Medicine, 2(1). http://doi.org/10.23937/2378-3516/1410016
  • Aslan, E., & Özüpak, Y. (2024). Classification of Blood Cells with Convolutional Neural Network Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(1), 314–326. https://doi.org/10.17798/bitlisfen.1401294
  • Bakator, M., & Radosav, D. (2018). Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction, 2(3), 47. https://doi.org/10.3390/mti2030047
  • Berliner, D., Schneider, N., Welte, T., & Bauersachs, J. (2016). The differential diagnosis of dyspnoea. Deutsches Ärzteblatt International, 113(49), 834–844. https://doi.org/10.3238%2Farztebl.2016.0834
  • Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & de Albuquerque, V. H. C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559. https://doi.org/10.3390/app10020559
  • Chowdary, G. J., Suganya, G., Premalatha, M., & Karunamurthy, K. (2021). Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays. In: M. Sabharwal, B. B. Balusamy, S. R. Kumar, N. Gayathri, & S. Suvanov (Eds.), Applications of Artificial Intelligence in E-Healthcare Systems, (pp. 37-54). https://doi.org/10.1049/PBHE040E_ch3
  • Dey, N., Zhang, Y.-D., Rajinikanth, V., Pugalenthi, R., & Raja, N. S. M. (2021). Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recognition Letters, 143, 67–74. https://doi.org/10.1016/j.patrec.2020.12.010
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N. (2021, May 3-7). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021), (pp. 1-21). https://iclr.cc/virtual/2021/poster/3013
  • Gabruseva, T., Poplavskiy, D., & Kalinin, A. (2020, June 14-19). Deep Learning for Automatic Pneumonia Detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 1436-1443), Seattle, WA, USA. https://doi.org/10.1109/CVPRW50498.2020.00183
  • Guan, Q., Wang, Y., Ping. B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., & Xiang, J. (2019). Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. Journal of Cancer, 10(20), 4876–4882. https://doi.org/10.7150/jca.28769
  • Hassan, M. (2018, November 20). VGG16–Convolutional Network for Classification and Detection. https://neurohive.io/en/popularnetworks/vgg16
  • Koitka, S., & Friedrich, C. M. (2016, September 5-8). Traditional feature engineering and deep learning approaches at medical classification task of imageCLEF 2016. In: K. Balog, L. Cappellato, N. Ferro, & C. Macdonald (Eds.), Proceedings of the Conference and Labs of the Evaluation Forum (vol. 1609, pp. 304-317), Évora, Portugal.
  • Özüpak, Y. (2024). Detection of Malaria with Convolutional Neural Network (CNN) Architectures Using Cell Images. Cukurova University Journal of the Faculty of Engineering, 39(1), 197-210. https://doi.org/10.21605/cukurovaumfd.1460434
  • Pacal, I. (2023). A Vision Transformer-based Approach for Automatic COVID-19 Diagnosis on Chest X-ray Images. Journal of the Institute of Science and Technology, 13(2), 778–791. https://doi.org/10.21597/jist.1225156
  • Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Ball, R. L., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. https://doi.org/10.48550/arXiv.1711.05225
  • Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4-21. https://doi.org/10.1109/jbhi.2016.2636665
  • Salehinejad, H., Valaee, S., Dowdell, T., Colak, E., & Barfett, J. (2018, April 15-20). Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 990–994), Calgary, AB, Canada. https://doi.org/10.1109/ICASSP.2018.8461430
  • Singh, S., & Tripathi, B. K. (2022). Pneumonia classification using quaternion deep learning. Multimedia Tools and Applications, 81, 1743–1764. https://doi.org/10.1007/s11042-021-11409-7
  • Toğaçar, M., Ergen, B., Cömert, Z., & Özyurt, F.(2020). A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM, 41(4), 212–222. https://doi.org/10.1016/j.irbm.2019.10.006
  • Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019, February 20-22). Pneumonia Detection Using CNN based Feature Extraction. In: Proceedings of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1–7), Coimbatore, India. https://doi.org/10.1109/ICECCT.2019.8869364
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł, Polosukhin, I.(2017, December 4-9). Attention is all you need. In: I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach CA. https://doi.org/10.48550/arXiv.1706.03762
  • Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017, July 21-26). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3462-3471). https://doi.org/10.1109/CVPR.2017.369
  • Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Jiang, Z., Hou, Q., & Feng, J. (2021). Deepvit: Towards deeper vision transformer. https://doi.org/10.48550/arXiv.2103.11886
Year 2024, , 324 - 334, 29.06.2024
https://doi.org/10.54287/gujsa.1464311

Abstract

References

  • Akter, S., Shamsuzzaman, & Jahan, F. (2015). Community Acquired Pneumonia. International Journal of Respiratory and Pulmonary Medicine, 2(1). http://doi.org/10.23937/2378-3516/1410016
  • Aslan, E., & Özüpak, Y. (2024). Classification of Blood Cells with Convolutional Neural Network Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(1), 314–326. https://doi.org/10.17798/bitlisfen.1401294
  • Bakator, M., & Radosav, D. (2018). Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction, 2(3), 47. https://doi.org/10.3390/mti2030047
  • Berliner, D., Schneider, N., Welte, T., & Bauersachs, J. (2016). The differential diagnosis of dyspnoea. Deutsches Ärzteblatt International, 113(49), 834–844. https://doi.org/10.3238%2Farztebl.2016.0834
  • Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & de Albuquerque, V. H. C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559. https://doi.org/10.3390/app10020559
  • Chowdary, G. J., Suganya, G., Premalatha, M., & Karunamurthy, K. (2021). Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays. In: M. Sabharwal, B. B. Balusamy, S. R. Kumar, N. Gayathri, & S. Suvanov (Eds.), Applications of Artificial Intelligence in E-Healthcare Systems, (pp. 37-54). https://doi.org/10.1049/PBHE040E_ch3
  • Dey, N., Zhang, Y.-D., Rajinikanth, V., Pugalenthi, R., & Raja, N. S. M. (2021). Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recognition Letters, 143, 67–74. https://doi.org/10.1016/j.patrec.2020.12.010
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N. (2021, May 3-7). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021), (pp. 1-21). https://iclr.cc/virtual/2021/poster/3013
  • Gabruseva, T., Poplavskiy, D., & Kalinin, A. (2020, June 14-19). Deep Learning for Automatic Pneumonia Detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 1436-1443), Seattle, WA, USA. https://doi.org/10.1109/CVPRW50498.2020.00183
  • Guan, Q., Wang, Y., Ping. B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., & Xiang, J. (2019). Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. Journal of Cancer, 10(20), 4876–4882. https://doi.org/10.7150/jca.28769
  • Hassan, M. (2018, November 20). VGG16–Convolutional Network for Classification and Detection. https://neurohive.io/en/popularnetworks/vgg16
  • Koitka, S., & Friedrich, C. M. (2016, September 5-8). Traditional feature engineering and deep learning approaches at medical classification task of imageCLEF 2016. In: K. Balog, L. Cappellato, N. Ferro, & C. Macdonald (Eds.), Proceedings of the Conference and Labs of the Evaluation Forum (vol. 1609, pp. 304-317), Évora, Portugal.
  • Özüpak, Y. (2024). Detection of Malaria with Convolutional Neural Network (CNN) Architectures Using Cell Images. Cukurova University Journal of the Faculty of Engineering, 39(1), 197-210. https://doi.org/10.21605/cukurovaumfd.1460434
  • Pacal, I. (2023). A Vision Transformer-based Approach for Automatic COVID-19 Diagnosis on Chest X-ray Images. Journal of the Institute of Science and Technology, 13(2), 778–791. https://doi.org/10.21597/jist.1225156
  • Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Ball, R. L., Langlotz, C., Shpanskaya, K., Lungren, M. P., & Ng, A. Y. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. https://doi.org/10.48550/arXiv.1711.05225
  • Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4-21. https://doi.org/10.1109/jbhi.2016.2636665
  • Salehinejad, H., Valaee, S., Dowdell, T., Colak, E., & Barfett, J. (2018, April 15-20). Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 990–994), Calgary, AB, Canada. https://doi.org/10.1109/ICASSP.2018.8461430
  • Singh, S., & Tripathi, B. K. (2022). Pneumonia classification using quaternion deep learning. Multimedia Tools and Applications, 81, 1743–1764. https://doi.org/10.1007/s11042-021-11409-7
  • Toğaçar, M., Ergen, B., Cömert, Z., & Özyurt, F.(2020). A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM, 41(4), 212–222. https://doi.org/10.1016/j.irbm.2019.10.006
  • Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019, February 20-22). Pneumonia Detection Using CNN based Feature Extraction. In: Proceedings of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1–7), Coimbatore, India. https://doi.org/10.1109/ICECCT.2019.8869364
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł, Polosukhin, I.(2017, December 4-9). Attention is all you need. In: I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach CA. https://doi.org/10.48550/arXiv.1706.03762
  • Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017, July 21-26). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3462-3471). https://doi.org/10.1109/CVPR.2017.369
  • Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Jiang, Z., Hou, Q., & Feng, J. (2021). Deepvit: Towards deeper vision transformer. https://doi.org/10.48550/arXiv.2103.11886
There are 23 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Engineering Practice and Education
Authors

Emrah Aslan 0000-0002-0181-3658

Early Pub Date June 14, 2024
Publication Date June 29, 2024
Submission Date April 4, 2024
Acceptance Date May 6, 2024
Published in Issue Year 2024

Cite

APA Aslan, E. (2024). Diagnosis of Pneumonia from Chest X-ray Images with Vision Transformer Approach. Gazi University Journal of Science Part A: Engineering and Innovation, 11(2), 324-334. https://doi.org/10.54287/gujsa.1464311