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OmniScience: A Multi-disciplinary Journal Review Article IMSEMTI 2024

Advancements in Pneumonia X-Ray Image Detection: A Review

  1. Shivani Sharma Department of Computer Science & Engineering, Himachal Pradesh Technical University, Sasan
  2. Avni Sharma Department of Computer Science & Engineering, Himachal Pradesh Technical University, Sasan

Abstract

Pneumonia remains a primary cause of morbidness and mortality worldwide, necessitating the continuous advancement of diagnostic techniques for timely and accurate detection. Pneumonia is common, it is potentially a life-threatening infection for respiration, poses significant challenges to healthcare systems worldwide. Recently, the arrival of deep learning techniques has stirred up the field of medical imaging, offering promising avenues for enhanced pneumonia detection. In this paper, the advancements in pneumonia detection facilitated by deep learning techniques and their applications are discussed, and exploration is done that how deep learning techniques are changing the game. Beginning with an insightful introduction we dive into different deep learning methods, showing how they work and where they shine. This paper encompasses a thorough examination of diverse deep learning methods employed in pneumonia detection. This paper analyzes the performance of different techniques across different datasets, providing a comparative assessment of available research. By unpacking the latest advancements in a clear and friendly way, this review is valuable for anyone interested in using deep learning technology to detect and identify pneumonia effectively.

Keywords

References (24)

  1. Kundu R, Das R, Geem ZW, Han GT, Sarkar R. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLOS ONE. 2021;16(9):e0256630. doi:10.1371/journal.pone.0256630
  2. Kumar S, Singh P, Ranjan M. A review on deep learning based pneumonia detection systems. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). 2021:289-296. doi:10.1109/icais50930.2021.9395868
  3. Gm H, Gourisaria MK, Rautaray SS, Pandey M. Pneumonia detection using CNN through chest x-ray. 2021.
  4. Prasetyo SY, Nabiilah GZ, Izdihar ZN, Isa SM. Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models. International Journal of Advances in Intelligent Informatics. 2023;9(2):319. doi:10.26555/ijain.v9i2.884
  5. IEEE Staff. 2017 Nirma University International Conference on Engineering (NUiCONE). IEEE; 2017.
  6. İstanbul AREL Üniversitesi, IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers. Turkey Section, and Institute of Electrical and Electronics Engineers, Scientific Meeting on Electrical-Electronics, Computer and Biomedical Engineering : 24–26 April 2019 : Istanbul AREL University, Kemal Gözükara Campus - Prof. Dr. Aziz Sancar Conference Hall = Uluslararası Katılımlı Elektrik-Elektronik, Bilgisayar, Biyomedikal Mühendislikleri Bilimsel Toplantısı : 24–26 Nisan 2019 : T. C. İstanbul AREL Üniversitesi, Kemal Gözükara Yerlekeşi - Prof. Dr. Aziz Sancar Amfisi.
  7. Amity University and Institute of Electrical and Electronics Engineers, 10th International Conference on Cloud Computing, Data Science & Engineering : proceedings of the Confluence 2020 : 29–31 January 2020, Amity University, Uttar Pradesh, India.
  8. Yi R, Tang L, Tian Y, Liu J, Wu Z. Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework. Neural Computing and Applications. 2021;35(20):14473-14486. doi:10.1007/s00521-021-06102-7
  9. Hertel R, Benlamri R. A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis. Biomedical Engineering Advances. 2022;3:100041. doi:10.1016/j.bea.2022.100041
  10. Varshni D, Nijhawan R, Thakral K, Mittal A, Agarwal L. Pneumonia Detection Using CNN based Feature Extraction. 2019.
  11. Gazi Üniversitesi, Aksaray Üniversitesi, University of Buner, P. International Islamic University (Islāmābād, Institute of Electrical and Electronics Engineers. Turkey Section, and Institute of Electrical and Electronics Engineers, 2nd International Conference on Electrical, Communication, and Computer Engineering (ICECCE 2020): 12th–13th June 2020, Istanbul, Turkey.
  12. Padash S, Mohebbian MR, Adams SJ, Henderson RDE, Babyn P. Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review. Pediatric Radiology. 2022;52(8):1568-1580. doi:10.1007/s00247-022-05368-w
  13. Alshmrani GMM, Ni Q, Jiang R, Pervaiz H, Elshennawy NM. A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alexandria Engineering Journal. 2023;64:923-935. doi:10.1016/j.aej.2022.10.053
  14. Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology, Gorakhpur, India., Verma* G, Prakash S, Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology, Gorakhpur, India. Pneumonia Classification using Deep Learning in Healthcare. International Journal of Innovative Technology and Exploring Engineering. 2020;9(4):1715-1723. doi:10.35940/ijitee.d1599.029420
  15. El Asnaoui K. Design ensemble deep learning model for pneumonia disease classification. International Journal of Multimedia Information Retrieval. 2021;10(1):55-68. doi:10.1007/s13735-021-00204-7
  16. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:770-778. doi:10.1109/cvpr.2016.90
  17. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: Inverted residuals and linear bottlenecks.
  18. Faizabadi AR, Motakabber S, Ibrahimy M, Ansari N, Motakabber SMA, Ibrahimy MI. Effective pneumonia detection using ResNet based transfer learning effective pneumonia detection using ResNet based transfer learning. Available: https://www.researchgate.net/publication/340096057
  19. Liang G, Zheng L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Computer Methods and Programs in Biomedicine. 2020;187:104964. doi:10.1016/j.cmpb.2019.06.023
  20. Jain R, Nagrath P, Kataria G, Sirish Kaushik V, Jude Hemanth D. Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement. 2020;165:108046. doi:10.1016/j.measurement.2020.108046
  21. Stephen O, Sain M, Maduh UJ, Jeong DU. An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. Journal of Healthcare Engineering. 2019;2019:1-7. doi:10.1155/2019/4180949
  22. Elshennawy NM, Ibrahim DM. Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images. Diagnostics. 2020;10(9):649. doi:10.3390/diagnostics10090649
  23. Labhane G, Pansare R, Maheshwari S, Tiwari R, Shukla A. Detection of Pediatric Pneumonia from Chest X-Ray Images using CNN and Transfer Learning. 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE). 2020:85-92. doi:10.1109/icetce48199.2020.9091755
  24. 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. Applied Sciences. 2022;12(13):6448. doi:10.3390/app12136448
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