• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images
  • VIEWS 176
  • PDF 55

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i8.3151

Year: 2024, Volume: 17, Issue: 8, Pages: 702-712

Original Article

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images

Mamta Patel1*, Mehul Shah2

1Research Scholar, Instrumentation and Control Engineering, Gujarat Technological University, Gujarat, India
2Associate Professor, Instrumentation and Control Engineering, Vishwakarma Government Engineering College, Gujarat, India

*Corresponding Author
Email: [email protected]

Received Date:13 December 2023, Accepted Date:22 January 2024, Published Date:14 February 2024

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments.

Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest X­rays (CXR), Lung diseases

References

  2. Rehman A, Saba T, Tariq U, Ayesha N. Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons. IT Professional. 2021;23(3):63–68. Available from: https://doi.org/10.1109/MITP.2020.3036820
  3. Zak M, Krzyżak A. Classification of Lung Diseases Using Deep Learning Models. In: International Conference on Computational Science- ICCS 2020, Lecture Notes in Computer Science . Springer, Cham. 12139:621–634.
  4. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data. 2021;8(1):1–74. Available from: https://doi.org/10.1186/s40537-021-00444-8
  5. Goyal S, Singh R. Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing. 2023;14(4):3239–3259. Available from: https://doi.org/10.1007/s12652-021-03464-7
  6. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim OA, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine. 2020;121:1–11. Available from: https://doi.org/10.1016/j.compbiomed.2020.103792
  8. Hussain E, Hasan M, Rahman MA, Lee I, Tamanna T, Parvez MZ. CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos, Solitons & Fractals. 2021;142:1–12. Available from: https://doi.org/10.1016/j.chaos.2020.110495
  10. Nahiduzzaman M, Islam MR, Hassan R. ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network. Expert Systems with Applications. 2023;211:1–14. Available from: https://doi.org/10.1016/j.eswa.2022.118576
  11. 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. Available from: https://doi.org/10.1016/j.aej.2022.10.053
  12. Al-Sheikh MH, Dandan OA, Al-Shamayleh AS, Jalab HA, Ibrahim RW. Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images. Scientific Reports. 2023;13(1):1–14. Available from: https://doi.org/10.1038/s41598-023-46147-3
  13. Kaya M, Eris M. D3SENet: A hybrid deep feature extraction network for Covid-19 classification using chest X-ray images. Biomedical Signal Processing and Control. 2023;82:1–10. Available from: https://doi.org/10.1016/j.bspc.2022.104559
  14. Farhan AMQ, Yang S. Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. Multimedia Tools and Applications. 2023;82(25):38561–38587. Available from: https://doi.org/10.1007/s11042-023-15047-z
  16. Asuntha A, Srinivasan A. Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications. 2020;79:7731–7762. Available from: https://doi.org/10.1007/s11042-019-08394-3
  17. Bokade AN, Shah A. Classification of Mammography Images Using Deep-CNN based Feature Ensemble Approach and its Implementation on a Low-Cost Raspberry Pi. International Journal of Computing and Digital Systems. 2023;13(1):1451–1463. Available from: http://dx.doi.org/10.12785/ijcds/1301117
  18. Patel M, Shah M. Transfer learning with fine-tuned deep CNN model for COVID-19 diagnosis from chest X-ray images. International Journal of Advanced Technology and Engineering Exploration. 2023;10(103):720–740. Available from: https://doi.org/10.19101/IJATEE.2022.10100044
  19. Asnaoui KE. Design ensemble deep learning model for pneumonia disease classification. International Journal of Multimedia Information Retrieval. 2021;10(1):55–68. Available from: https://doi.org/10.1007/s13735-021-00204-7
  20. Velu S. An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images. Mathematical Biosciences and Engineering. 2023;20(5):8400–8427. Available from: https://doi.org/10.3934/mbe.2023368
  21. Kim S, Rim B, Choi S, Lee A, Min S, Hong M. Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images. Diagnostics. 2022;12(4). Available from: https://doi.org/10.3390/diagnostics12040915
  22. Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence. 2021;51(2):854–864. Available from: https://doi.org/10.1007/s10489-020-01829-7
  23. Khan AI, Shah JL, Bhat MM. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine. 2020;196:1–9. Available from: https://doi.org/10.1016/j.cmpb.2020.105581

Copyright

© 2024 Patel & Shah. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

  • 14 February 2024

Year: 2024, Volume: 17, Issue: 8

Article Metrics


Post Graduate Fellowship in Research Communication

More articles

DON'T MISS OUT!

Subscribe now for latest articles and news.