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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

Author(s): Somya Srivastava, Seema Verma, Nripendra Narayan Das*, Shraddha Sharma and Gaurav Dubey

Volume 17, Issue 3, 2024

Published on: 11 December, 2023

Article ID: e111223224354 Pages: 12

DOI: 10.2174/0126662558269484231121112300

Price: $65

Abstract

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays.

Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study.

Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation.

Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%.

Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models.

Graphical Abstract

[1]
O. Ruuskanen, E. Lahti, L.C. Jennings, and D.R. Murdoch, "Viral pneumonia", Lancet, vol. 377, no. 9773, pp. 1264-1275, 2011.
[http://dx.doi.org/10.1016/S0140-6736(10)61459-6] [PMID: 21435708]
[2]
L.A. Mandell, R.G. Wunderink, A. Anzueto, J.G. Bartlett, G.D. Campbell, N.C. Dean, S.F. Dowell, T.M. File Jr, D.M. Musher, M.S. Niederman, A. Torres, and C.G. Whitney, "Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults", Clin. Infect. Dis., vol. 44, no. Suppl 2, p. S27-S72, 2007.
[http://dx.doi.org/10.1086/511159] [PMID: 17278083]
[3]
R.G. Wunderink, and G. Waterer, "Advances in the causes and management of community acquired pneumonia in adults", BMJ, vol. 358, p. j2471, 2017.
[http://dx.doi.org/10.1136/bmj.j2471] [PMID: 28694251]
[4]
H.Y.F. Wong, H.Y.S. Lam, A.H.T. Fong, S.T. Leung, T.W.Y. Chin, C.S.Y. Lo, M.M.S. Lui, J.C.Y. Lee, K.W.H. Chiu, T.W.H. Chung, E.Y.P. Lee, E.Y.F. Wan, I.F.N. Hung, T.P.W. Lam, M.D. Kuo, and M.Y. Ng, "Frequency and distribution of chest radiographic findings in patients positive for COVID-19", Radiology, vol. 296, no. 2, pp. E72-E78, 2020.
[http://dx.doi.org/10.1148/radiol.2020201160] [PMID: 32216717]
[5]
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning", Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[6]
G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A.W.M. van der Laak, B. van Ginneken, and C.I. Sánchez, "A survey on deep learning in medical image analysis", Med. Image Anal., vol. 42, pp. 60-88, 2017.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[7]
T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images", Comput. Biol. Med., vol. 121, p. 103792, 2020.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103792] [PMID: 32568675]
[8]
L. Wang, Z.Q. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images", Sci. Rep., vol. 10, no. 1, p. 19549, 2020.
[http://dx.doi.org/10.1038/s41598-020-76550-z] [PMID: 33177550]
[9]
I.D. Apostolopoulos, and T.A. Mpesiana, "COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks", Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 635-640, 2020.
[http://dx.doi.org/10.1007/s13246-020-00865-4] [PMID: 32524445]
[10]
S. Singh, S.S. Rawat, M. Gupta, B.K. Tripathi, F. Alanzi, A. Majumdar, P. Khuwuthyakorn, and O. Thinnukool, Deep attention network for pneumonia detection using Chest X-Ray images., vol. 74. 2023, no. 1, pp. 1673-1691.
[http://dx.doi.org/10.32604/cmc.2023.032364]
[11]
M. Bhandari, T.B. Shahi, B. Siku, and A. Neupane, "Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI", Comput. Biol. Med., vol. 150, p. 106156, 2022.
[http://dx.doi.org/10.1016/j.compbiomed.2022.106156]
[12]
F. Hajjej, S. Ayouni, M. Hasan, and T. Abir, "Automatic detection of cases of COVID-19 pneumonia from chest X-ray images and deep learning approaches", Comput. Intell. Neurosci., vol. 2022, p. 7451551, 2022.
[http://dx.doi.org/10.1155/2022/7451551]
[13]
C.A. Ortiz-Toro, A. Garcia-Pedrero, M. Lillo-Saavedra, and C. Gonzalo-Martin, Textural features for automatic detection and categorisation of pneumonia in chest X-ray images.In: IEEE 35th Int., Sympo. Comp. Based Med. Syst: Shenzen, China, 2022, pp. 19-24.
[http://dx.doi.org/10.1109/CBMS55023.2022.00011]
[14]
K. Aktas, V. Ignjatovic, D. Ilic, M. Marjanovic, and G. Anbarjafari, "Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset", Signal Image Video Process., vol. 17, no. 4, pp. 1035-1041, 2023.
[http://dx.doi.org/10.1007/s11760-022-02309-w] [PMID: 35873389]
[15]
P. Saxena, S.K. Singh, G. Tiwary, Y. Mittal, and I. Jain, "An artificial intelligence technique for COVID-19 detection with eXplainability using Lungs X-Ray Images", In IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballari, India, 2022, pp. 1-6
[http://dx.doi.org/10.1109/ICDCECE53908.2022.9793240]
[16]
D. Lv, Y. Wang, S. Wang, Q. Zhang, W. Qi, Y. Li, and L. Sun, "A Cascade-SEME network for COVID-19 detection in chest X-ray images", Med. Phys., vol. 48, no. 5, pp. 2337-2353, 2021.
[http://dx.doi.org/10.1002/mp.14711] [PMID: 33778966]
[17]
H.I. Hussein, A.O. Mohammed, M.M. Hassan, and R.J. Mstafa, "Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images", Expert Syst. Appl., vol. 223, p. 119900, 2023.
[http://dx.doi.org/10.1016/j.eswa.2023.119900] [PMID: 36969370]
[18]
A. Iqbal, M. Usman, and Z. Ahmed, "Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach", Biomed. Signal Process. Control, vol. 84, p. 104667, 2023.
[http://dx.doi.org/10.1016/j.bspc.2023.104667]
[19]
W. Kusakunniran, P. Borwarnginn, T. Siriapisith, S. Karnjanapreechakorn, K. Sutassananon, T. Tongdee, and P. Saiviroonporn, "Detecting COVID-19 in chest X-ray images", IJECE, vol. 13, no. 3, pp. 3290-3298, 2023.
[http://dx.doi.org/10.11591/ijece.v13i3.pp3290-3298]
[20]
D.E. Cahyani, A.D. Hariadi, F.F. Setyawan, L. Gumilar, and S. Setumin, "COVID-19 classification using CNN-BiLSTM based on chest X-ray images", Bull. Electr. Eng. Inform., vol. 12, no. 3, pp. 1773-1782, 2023.
[http://dx.doi.org/10.11591/eei.v12i3.4848]
[21]
D. Mitra, P. Rakshit, A. Jha, D. Dugar, and K. Iqbal, "Lung disease prediction using deep learning", Adv. Commun., Devices Network.: Proceed. ICCDN 2021, 2023.
[http://dx.doi.org/10.1007/978-981-19-2004-2_40]
[22]
M. Nahiduzzaman, M.R. Islam, and R. Hassan, "ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network", Expert Syst. Appl., vol. 211, p. 118576, 2023.
[http://dx.doi.org/10.1016/j.eswa.2022.118576] [PMID: 36062267]
[23]
S.H. Karaddi, and L.D. Sharma, "Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks", Expert Syst. Appl., vol. 211, p. 118650, 2023.
[http://dx.doi.org/10.1016/j.eswa.2022.118650]
[24]
T. Agrawal, and P. Choudhary, "ALCNN: Attention based lightweight convolutional neural network for pneumothorax detection in chest X-rays", Biomed. Signal Process. Control, vol. 79, p. 104126, 2023.
[http://dx.doi.org/10.1016/j.bspc.2022.104126]
[25]
Li. Yao, E. Poblenz, D. Dagunts, B. Covington, D. Bernard, and K. Lyman, "Learning to diagnose from scratch by exploiting dependencies among labels", arXiv, 2017.
[26]
H. Boussaid, and I. Kokkinos, "Fast and exact: ADMM-based discriminative shape segmentation with loopy part models", In Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014.
[http://dx.doi.org/10.1109/CVPR.2014.517]

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