Detection of Tuberculosis Disease in Lung X-ray Images Using the DenseNet121 Method

Madyono Madyono, Anisah Nabilah

Abstract


Tuberculosis (TB) is a lung infection caused by Mycobacterium tuberculosis and can be detected through chest X-ray imaging. In this study, tuberculosis disease detection was carried out using the DenseNet121 method, a deep-learning architecture proven effective in medical image classification tasks. This study used a dataset of 4,200 lung X-ray images classified as positive or negative for TB. The DenseNet121 model was trained with this data to identify patterns in the X-ray images indicating tuberculosis infection. The results of the model evaluation showed high performance with a precision value of 0.91, a recall of 0.90, and an f1-score of 0.89. In addition, the model achieved an overall accuracy of 90.4%. The results of this study indicate that the DenseNet121 method can be a reliable tool in detecting tuberculosis from chest X-ray images so that it can assist medical personnel in the diagnosis process more quickly and accurately.


Keywords


Tuberculosis, Lung X-ray, Deep Learning, DenseNet121, Disease Detection

Full Text:

PDF

References


Abdullahi Ibrahim et al., "Automated detection of Mycobacterium tuberculosis using transfer learning", The Journal of Infection in Developing Countries, vol. 15, pp. 678-686, May 2021.

Global Tuberculosis Report 2018. Licence: CC BY-NC-SA 3.0 IGO, 2018, [online] Available: https://creativecommons.org/licenses/by-nc-sa/3.0/igo, ISBN 978-92-4-156564-6.

World Health Organization. (2021). Global tuberculosis report 2021. World Health Organization.

Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis using convolutional neural networks. Radiology, 284(2), 574-582.

L. Racic, T. Popovic, S. Cakic and S. Sandi, "Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network", 2021 25th International Conference on Information Technology (IT), 2021.

Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225..

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.

C. Vogler and D. Metaxas, "Parallel hidden markov models for american sign language recognition", Computer Vision 1999. The Proceedings of the Seventh IEEE International Conference on, vol. 1, pp. 116-122, 1999..

Vikas Mittal and R. K. Sharma, "Machine learning approach for classification of Parkinson disease using acoustic features", J. Reliab. Intell. Environ., vol. 7, no. 3, pp. 233-239, 2021.

Lavanya Madhuri Bollipo and K. V Kadambari, "Fast and robust supervised machine learning approach for classification and prediction of Parkinson’s disease onset", Comput. methods Biomech. Biomed. Eng. Imaging Vis., vol. 9, no. 6, pp. 690-706, 2021.

Lecun et al, "Gradient based learning applied to document recognition", Proceedings of IEEE, no.86 November, pp.2278-2324, 1998

Mehera Binte Mizan, Md Al Mehedi Hasan and Syed Rahat Hassan, "A comparative study of tuberculosis detection using deep convolutional neural network", 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), pp. 157-161, 2020.

Mohammed Salih Ahmed, Atta Rahman, Faris AlGhamdi, Saleh AlDakheel, Hammam Hakami, Ali AlJumah, et al., "Joint diagnosis of pneumonia covid-19 and tuberculosis from chest x-ray images: A deep learning approach", Diagnostics, vol. 13, no. 15, pp. 2562, 2023.

Lili Li, Shujuan Zhang and Bin Wang, "Plant disease detection and classification by deep learning-a review", IEEE Access, vol. 9, no. 2021, pp. 56683-56698.

Krizhevsky, Alex, Ilya Sutskever, dan Geoffrey E. Hinton, "Imagenet classification with deep convolutional neural networks.", Communications of the ACM 60.6 84-90, 2017.

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708.

B. Ananthakrishnan, A. Shaik, S. Kumar, S. O. Narendran, K. Mattu and M. S. Kavitha, "Automated detection and classification of oral squamous cell carcinoma using deep neural networks", Diagn, vol. 13, no. 5, pp. 918, 2023.

K. Shaheed, Q. Abbas, A. Hussain and I. Qureshi, "Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images", Diagnostics, vol. 13, pp. 2583, 2023.

S. M. Fati, E. M. Senan and Y. Javed, "Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches", Diagn, vol. 12, pp. 1899, 2022.

Dinesh Jackson Samuel and Rajesh Kanna Baskaran, Design to automate the detection and counting of Tuberculosis(TB) bacilli, pp. 11432, May 2021, [online] Available: https://doi.org/10.48550/arXiv.2105.11432.




DOI: https://doi.org/10.52088/ijesty.v5i2.853

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Madyono, Anisah Nabilah

International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674