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PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images

Year 2024, Volume: 19 Issue: 2, 325 - 338, 30.09.2024
https://doi.org/10.55525/tjst.1411197

Abstract

Pneumonia is a dangerous disease that causes severe inflammation of the air sacs in the lungs. It is one of the infectious diseases with high morbidity and mortality in all age groups worldwide. Chest X-ray (CXR) is a diagnostic and imaging modality widely used in diagnosing pneumonia due to its low dose of ionizing radiation, low cost, and easy accessibility. Many deep learning methods have been proposed in various medical applications to assist clinicians in detecting and diagnosing pneumonia from CXR images. We have proposed a novel PneumoNet using a convolutional neural network (CNN) to detect pneumonia using CXR images accurately. Transformer-based deep learning methods, which have yielded high performance in natural language processing (NLP) problems, have recently attracted the attention of researchers. In this work, we have compared our results obtained using the CNN model with transformer-based architectures. These transformer architectures are vision transformer (ViT), gated multilayer perceptron (gMLP), MLP-mixer, and FNet. In this study, we have used the healthy and pneumonia CXR images from public and private databases to develop the model. Our developed PneumoNet model has yielded the highest accuracy of 96.50% and 94.29% for private and public databases, respectively, in detecting pneumonia accurately from healthy subjects.

Ethical Statement

This article is derived from the PhD thesis of the corresponding author Zehra Kadiroğlu. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Dicle. Date: 13.10.2021, Number: 421. We would like to thank Dicle University Faculty of Medicine, Department of Chest Diseases and Tuberculosis for their contribution to the study.

References

  • World Health Organization. “Pneumonia.” Erişim: 9 Aralık 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ pneumonia
  • Torres A, Cilloniz C, Niederman MS, Menéndez R, Chalmers JD, Wunderink RG, van der Poll T. Pneumonia. Nat Rev Dis Primers 2021;7(1):25.
  • Kumar S, Singh P, Ranjan M. A review on deep learning based pneumonia detection systems. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, Coimbatore, India: IEEE.pp. 289-296.
  • Kwon T, Lee SP, Kim D, Jang J, Lee M, Kang SU, Kim H, Oh K, On J, Kim YJ, Yun SJ, Jin KN, Kim EY, Kim KG. Diagnostic performance of artificial intelligence model for pneumonia from chest radiography. PLoS One 2021;16(4):e0249399.
  • Mujahid M, Rustam F, Álvarez R, Luis Vidal Mazón J, Díez IT, Ashraf I. Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network. Diagnostics 2022;12(5):1280.
  • Govindarajan A, Govindarajan A, Tanamala S, Chattoraj S, Reddy B, Agrawal R, Iyer D, Srivastava A, Kumar P, Putha P. Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study. Diagnostics 2022; 12(11):2724.
  • Cha S-M, Lee S-S, Ko B. Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images. Appl Sci 2021; 11(3):1242.
  • Al Mamlook RE, Chen S, Bzizi, HF. Investigation of the performance of machine learning classifiers for pneumonia detection in chest X-ray images. In: 2020 IEEE International Conference on Electro Information Technology (EIT); 2020, Chicago, IL, USA, IEEE: pp. 98-104.
  • Bai Y, Mei J, Yuille A L, Xie C. Are transformers more robust than cnns?. Advances in Neural Information Processing Systems, 2021; 34:26831-26843.
  • Usman M, Zia T, Tariq A. Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography. J Digit Imaging 2022;35(6):1445-1462.
  • Vaswani A, Shazeer NM, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is All you Need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, USA, 2017, pp. 5998-6008.
  • Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In: 9th International Conference on Learning Representations (ICLR); 2021, Virtual Event, Austria, arXiv:2010.11929.
  • Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172(5):1122-1131.e9.
  • Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: hospitalscale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017; Honolulu, HI, USA: IEEE, pp. 3462-3471.
  • Ukwuoma CC, Qin Z, Belal Bin Heyat M, Akhtar F, Bamisile O, Muaad AY, Addo D, Al-Antari MA. A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res 2023; 48: 191-211.
  • Singh S, Rawat S, Gupta M, Tripathi B, Alanzi F, Majumdar A, Khuwuthyakorn P, Thinnukool O. Deep attention network for pneumonia detection using chest x-ray ımages. Comput Mater Contin 2023; 74(1): 1673-1691.
  • Tyagi K, Pathak G, Nijhawan R, Mittal A. Detecting pneumonia using vision transformer and comparing with other techniques. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2021; Coimbatore, India, IEEE: pp. 12-16.
  • Ma Y, Lv W. Identification of Pneumonia in Chest X-Ray Image Based on Transformer. Int J Antennas Propag 2022; 2022(1): 5072666.
  • Jıang Z, Chen L. Multisemantic level patch merger vision transformer for diagnosis of pneumonia. Comput Math Methods Med 2022; 2022(1): 7852958.
  • Wei X, Niu X, Zhang X, Li Y. Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays. In: 2022 IEEE International Conference on Big Data (Big Data); 2022; IEEE. pp. 5361-5369.
  • Okolo GI, Katsigiannis S, Ramzan N. IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification. Comput Methods Programs Biomed 2022; 226:107141.
  • Mabrouk A, Díaz Redondo RP, Dahou A, Abd Elaziz M, Kayed M. Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks. Appl Sci 2022; 12(13):6448.
  • Gokul AG, Kumaratharan N, Rani PL, Devi N. Ensembling Framework for Pneumonia Detection in Chest X-ray images. In: 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN); 2022; Villupuram, India: IEEE, pp. 1-5.
  • Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Keysers D, Uszkoreit J, Lucic M, & Dosovitskiy A. MLP-Mixer: An all-MLP Architecture for Vision. In NeurIPS 2021, 34: 24261-24272.
  • Lee-Thorp J, Ainslie J, Eckstein I, Ontanon S. FNet: Mixing tokens with Fourier transforms. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2022; Seattle, United States: Association for Computational Linguistics, pp. 4296-4313.
  • Liu H, Dai Z, So D, Le QV. Pay attention to mlps. In NeurIPS 2021; 34:9204-9215.
  • Visuña L, Yang D, Garcia-Blas J, Carretero J. Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Med Imag 2022; 22(1): 1-16.
  • Jain DK, Singh T, Saurabh P, Bisen D, Sahu N, Mishra J, Rahman H. Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans. Comput Intell Neurosci 2022;2022(1): 7474304.
  • Şengür D. Investigation of the relationships of the students’ academic level and gender with Covid-19 based anxiety and protective behaviors: A data mining approach. Turkish J Sci Technol 2020; 15(2): 93-99.
  • Şengür D, Siuly S. Efficient approach for EEG‐based emotion recognition. Electron Lett 2020; 56(25): 1361-1364.
  • Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Comput Methods Programs Biomed 2022; 226:107161.
  • Sobahi N, Atila O, Deniz E, Sengur A, Acharya UR. Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds. Biocybern Biomed Eng 2022; 42(3):1066-1080.

PneumoNet: Göğüs Röntgeni Görüntülerinden Derin Sinir Ağları Kullanarak Pnömoninin Otomatik Tespiti

Year 2024, Volume: 19 Issue: 2, 325 - 338, 30.09.2024
https://doi.org/10.55525/tjst.1411197

Abstract

Pnömoni, akciğerlerdeki hava keseciklerinin şiddetli iltihaplanmasına neden olan tehlikeli bir hastalıktır. Dünya genelinde tüm yaş gruplarında yüksek morbidite ve mortaliteye sahip bulaşıcı hastalıklardan biridir. Göğüs röntgeni (CXR), düşük iyonize radyasyon dozu, düşük maliyeti ve kolay erişilebilirliği nedeniyle pnömoni teşhisinde yaygın olarak kullanılan bir teşhis ve görüntüleme yöntemidir. Çeşitli tıbbi uygulamalarda klinisyenlere CXR görüntülerinden pnömoni tespit ve teşhisinde yardımcı olmak için birçok derin öğrenme yöntemi önerilmiştir. CXR görüntülerini kullanarak pnömoniyi doğru bir şekilde tespit etmek için evrişimsel sinir ağı (ESA) kullanan yeni bir PneumoNet önerilmiştir. Doğal dil işleme (NLP) problemlerinde yüksek performans sağlayan dönüştürücü tabanlı derin öğrenme yöntemleri son zamanlarda araştırmacıların ilgisini çekmektedir. Bu çalışmada, CNN modelini kullanarak elde ettiğimiz sonuçlar dönüştürücü tabanlı mimarilerle karşılaştırılmıştır. Bu dönüştürücü mimariler görüntü dönüştürücü (ViT), kapılı çok katmanlı algılayıcı (gMLP), MLP-mikser ve FNet’tir. Bu çalışmada, modeli geliştirmek için kamu ve özel veri tabanlarından sağlıklı ve pnömoni CXR görüntüleri kullanılmıştır. Geliştirdiğimiz PneumoNet modeli, sağlıklı bireylerden pnömoniyi doğru bir şekilde tespit etmede özel ve kamu veri tabanları için sırasıyla %96,50 ve %94,29’luk en yüksek doğruluğu sağlamıştır.

References

  • World Health Organization. “Pneumonia.” Erişim: 9 Aralık 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ pneumonia
  • Torres A, Cilloniz C, Niederman MS, Menéndez R, Chalmers JD, Wunderink RG, van der Poll T. Pneumonia. Nat Rev Dis Primers 2021;7(1):25.
  • Kumar S, Singh P, Ranjan M. A review on deep learning based pneumonia detection systems. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, Coimbatore, India: IEEE.pp. 289-296.
  • Kwon T, Lee SP, Kim D, Jang J, Lee M, Kang SU, Kim H, Oh K, On J, Kim YJ, Yun SJ, Jin KN, Kim EY, Kim KG. Diagnostic performance of artificial intelligence model for pneumonia from chest radiography. PLoS One 2021;16(4):e0249399.
  • Mujahid M, Rustam F, Álvarez R, Luis Vidal Mazón J, Díez IT, Ashraf I. Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network. Diagnostics 2022;12(5):1280.
  • Govindarajan A, Govindarajan A, Tanamala S, Chattoraj S, Reddy B, Agrawal R, Iyer D, Srivastava A, Kumar P, Putha P. Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study. Diagnostics 2022; 12(11):2724.
  • Cha S-M, Lee S-S, Ko B. Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images. Appl Sci 2021; 11(3):1242.
  • Al Mamlook RE, Chen S, Bzizi, HF. Investigation of the performance of machine learning classifiers for pneumonia detection in chest X-ray images. In: 2020 IEEE International Conference on Electro Information Technology (EIT); 2020, Chicago, IL, USA, IEEE: pp. 98-104.
  • Bai Y, Mei J, Yuille A L, Xie C. Are transformers more robust than cnns?. Advances in Neural Information Processing Systems, 2021; 34:26831-26843.
  • Usman M, Zia T, Tariq A. Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography. J Digit Imaging 2022;35(6):1445-1462.
  • Vaswani A, Shazeer NM, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is All you Need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, USA, 2017, pp. 5998-6008.
  • Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In: 9th International Conference on Learning Representations (ICLR); 2021, Virtual Event, Austria, arXiv:2010.11929.
  • Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172(5):1122-1131.e9.
  • Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: hospitalscale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017; Honolulu, HI, USA: IEEE, pp. 3462-3471.
  • Ukwuoma CC, Qin Z, Belal Bin Heyat M, Akhtar F, Bamisile O, Muaad AY, Addo D, Al-Antari MA. A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res 2023; 48: 191-211.
  • Singh S, Rawat S, Gupta M, Tripathi B, Alanzi F, Majumdar A, Khuwuthyakorn P, Thinnukool O. Deep attention network for pneumonia detection using chest x-ray ımages. Comput Mater Contin 2023; 74(1): 1673-1691.
  • Tyagi K, Pathak G, Nijhawan R, Mittal A. Detecting pneumonia using vision transformer and comparing with other techniques. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2021; Coimbatore, India, IEEE: pp. 12-16.
  • Ma Y, Lv W. Identification of Pneumonia in Chest X-Ray Image Based on Transformer. Int J Antennas Propag 2022; 2022(1): 5072666.
  • Jıang Z, Chen L. Multisemantic level patch merger vision transformer for diagnosis of pneumonia. Comput Math Methods Med 2022; 2022(1): 7852958.
  • Wei X, Niu X, Zhang X, Li Y. Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays. In: 2022 IEEE International Conference on Big Data (Big Data); 2022; IEEE. pp. 5361-5369.
  • Okolo GI, Katsigiannis S, Ramzan N. IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification. Comput Methods Programs Biomed 2022; 226:107141.
  • Mabrouk A, Díaz Redondo RP, Dahou A, Abd Elaziz M, Kayed M. Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks. Appl Sci 2022; 12(13):6448.
  • Gokul AG, Kumaratharan N, Rani PL, Devi N. Ensembling Framework for Pneumonia Detection in Chest X-ray images. In: 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN); 2022; Villupuram, India: IEEE, pp. 1-5.
  • Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Keysers D, Uszkoreit J, Lucic M, & Dosovitskiy A. MLP-Mixer: An all-MLP Architecture for Vision. In NeurIPS 2021, 34: 24261-24272.
  • Lee-Thorp J, Ainslie J, Eckstein I, Ontanon S. FNet: Mixing tokens with Fourier transforms. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2022; Seattle, United States: Association for Computational Linguistics, pp. 4296-4313.
  • Liu H, Dai Z, So D, Le QV. Pay attention to mlps. In NeurIPS 2021; 34:9204-9215.
  • Visuña L, Yang D, Garcia-Blas J, Carretero J. Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Med Imag 2022; 22(1): 1-16.
  • Jain DK, Singh T, Saurabh P, Bisen D, Sahu N, Mishra J, Rahman H. Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans. Comput Intell Neurosci 2022;2022(1): 7474304.
  • Şengür D. Investigation of the relationships of the students’ academic level and gender with Covid-19 based anxiety and protective behaviors: A data mining approach. Turkish J Sci Technol 2020; 15(2): 93-99.
  • Şengür D, Siuly S. Efficient approach for EEG‐based emotion recognition. Electron Lett 2020; 56(25): 1361-1364.
  • Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Comput Methods Programs Biomed 2022; 226:107161.
  • Sobahi N, Atila O, Deniz E, Sengur A, Acharya UR. Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds. Biocybern Biomed Eng 2022; 42(3):1066-1080.
There are 32 citations in total.

Details

Primary Language English
Subjects Computing Applications in Health
Journal Section TJST
Authors

Zehra Kadiroğlu 0000-0002-2696-8138

Erkan Deniz 0000-0002-9048-6547

Mazhar Kayaoğlu 0000-0002-5807-9781

Hanifi Güldemir 0000-0003-0491-8348

Abdurrahman Şenyiğit 0000-0001-9603-2231

Abdülkadir Şengür 0000-0003-1614-2639

Publication Date September 30, 2024
Submission Date December 28, 2023
Acceptance Date April 18, 2024
Published in Issue Year 2024 Volume: 19 Issue: 2

Cite

APA Kadiroğlu, Z., Deniz, E., Kayaoğlu, M., Güldemir, H., et al. (2024). PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images. Turkish Journal of Science and Technology, 19(2), 325-338. https://doi.org/10.55525/tjst.1411197
AMA Kadiroğlu Z, Deniz E, Kayaoğlu M, Güldemir H, Şenyiğit A, Şengür A. PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images. TJST. September 2024;19(2):325-338. doi:10.55525/tjst.1411197
Chicago Kadiroğlu, Zehra, Erkan Deniz, Mazhar Kayaoğlu, Hanifi Güldemir, Abdurrahman Şenyiğit, and Abdülkadir Şengür. “PneumoNet: Automated Detection of Pneumonia Using Deep Neural Networks from Chest X-Ray Images”. Turkish Journal of Science and Technology 19, no. 2 (September 2024): 325-38. https://doi.org/10.55525/tjst.1411197.
EndNote Kadiroğlu Z, Deniz E, Kayaoğlu M, Güldemir H, Şenyiğit A, Şengür A (September 1, 2024) PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images. Turkish Journal of Science and Technology 19 2 325–338.
IEEE Z. Kadiroğlu, E. Deniz, M. Kayaoğlu, H. Güldemir, A. Şenyiğit, and A. Şengür, “PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images”, TJST, vol. 19, no. 2, pp. 325–338, 2024, doi: 10.55525/tjst.1411197.
ISNAD Kadiroğlu, Zehra et al. “PneumoNet: Automated Detection of Pneumonia Using Deep Neural Networks from Chest X-Ray Images”. Turkish Journal of Science and Technology 19/2 (September 2024), 325-338. https://doi.org/10.55525/tjst.1411197.
JAMA Kadiroğlu Z, Deniz E, Kayaoğlu M, Güldemir H, Şenyiğit A, Şengür A. PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images. TJST. 2024;19:325–338.
MLA Kadiroğlu, Zehra et al. “PneumoNet: Automated Detection of Pneumonia Using Deep Neural Networks from Chest X-Ray Images”. Turkish Journal of Science and Technology, vol. 19, no. 2, 2024, pp. 325-38, doi:10.55525/tjst.1411197.
Vancouver Kadiroğlu Z, Deniz E, Kayaoğlu M, Güldemir H, Şenyiğit A, Şengür A. PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images. TJST. 2024;19(2):325-38.