Email this article Comparative Study of CNN Techniques for Tuberculosis Detection Using Chest X-Ray Images from Indonesia
Abstract
Convolutional neural networks (CNNs) represent a popular deep learning approach for image classification tasks. They have been extensively employed in studies aimed at classifying tuberculosis (TB), coronavirus disease 2019 (COVID-19), and normal conditions on chest X-ray images. However, there is limited research utilizing Indonesian data, and the integration of CNN models into user-friendly interfaces accessible to healthcare professionals remains uncommon. This study addresses these gaps by employing three CNN architectures—AlexNet, LeNet, and a modified model—to classify TB, COVID-19, and normal condition images. Training data were sourced from both a local hospital in Indonesia (RSUP dr. Rivai Abdullah) and an additional dataset available online. Results indicate that AlexNet achieved the highest accuracy, with rates of 97.52%, 64.45%, and 92.43% on the Kaggle dataset, the RSUP dr. Rivai Abdullah dataset, and the combined dataset, respectively. Subsequently, this model was integrated into a user interface and deployed for testing using new data from the RSUP dr. Rivai Abdullah dataset. The web-based interface, powered by the Gradio library, successfully detected 7 out of 10 new cases with 70% accuracy. This implementation may enable medical professionals to make preliminary diagnoses
Keywords
References
Dean Shaban, “Tuberculosis (TB): Causes, Symptoms, Treatment.” https://www.webmd.com/lung/understanding-tuberculosis-basics/
W. H. Organization, “Global Tuberculosis Report 2022,” 2022.
O. Yadav, K. Passi, and C. K. Jain, “Using Deep Learning to Classify X-ray Images of Potential Tuberculosis Patients,” Proc. - 2018 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2018, no. September 2019, pp. 2368–2375, 2019, doi: 10.1109/BIBM.2018.8621525.
D. Verma, C. Bose, N. Tufchi, K. Pant, V. Tripathi, and A. Thapliyal, “An efficient framework for identification of Tuberculosis and Pneumonia in chest X-ray images using Neural Network,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 217–224, 2020, doi: 10.1016/j.procs.2020.04.023.
S. Stirenko et al., “Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation,” 2018 IEEE 38th Int. Conf. Electron. Nanotechnology, ELNANO 2018 - Proc., pp. 422–428, 2018, doi: 10.1109/ELNANO.2018.8477564.
S. Aulia and S. Hadiyoso, “Tuberculosis Detection in X-Ray Image Using Deep Learning Approach with VGG-16 Architecture,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 8, no. 2, p. 290, 2022, doi: 10.26555/jiteki.v8i2.23994.
T. Rahman et al., “Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization,” IEEE Access, vol. 8, pp. 191586–191601, 2020, doi: 10.1109/ACCESS.2020.3031384.
K. Munadi, K. Muchtar, N. Maulina, and B. Pradhan, “Image Enhancement for Tuberculosis Detection Using Deep Learning,” IEEE Access, vol. 8, pp. 217897–217907, 2020, doi: 10.1109/ACCESS.2020.3041867.
N. Fitriyah, L. D. Kurniawati, E. Purwanti, and S. D. Astuti, “Pulmonary Disease Pattern Recognition on X-Ray Radiography Image Using Artificial Neural Network (ANN) Method,” J. Phys. Conf. Ser., vol. 1505, no. 1, 2020, doi: 10.1088/1742-6596/1505/1/012065.
- Arizal, A. Achmad, and A. D. Achmad, “Backpropagation Performance Against Support Vector Machine in Detecting Tuberculosis Based on Lung X-Ray Image,” vol. 165, no. ICMEMe 2018, pp. 84–88, 2019, doi: 10.2991/icmeme-18.2019.19.
T. Tri Saputra Sibarani and C. Author, “Analysis K-Nearest Neighbors (KNN) in Identifying Tuberculosis Disease (Tb) By Utilizing Hog Feature Extraction,” Int. Comput. Sci. Inf. Technol. JournalISSN, vol. 1, no. 1, pp. 33–38, 2020.
O. Hrizi et al., “Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model,” J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/8950243.
Z. Ul Abideen et al., “Uncertainty assisted robust tuberculosis identification with bayesian convolutional neural networks,” IEEE Access, vol. 8, pp. 22812–22825, 2020, doi: 10.1109/ACCESS.2020.2970023.
M. Owais, M. Arsalan, T. Mahmood, Y. H. Kim, and K. R. Park, “Comprehensive computer-aided decision support framework to diagnose tuberculosis from chest X-Ray images: Data mining study,” JMIR Med. Informatics, vol. 8, no. 12, pp. 1–24, 2020, doi: 10.2196/21790.
S. M. Fati, E. M. Senan, and N. ElHakim, “Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion,” Appl. Sci., vol. 12, no. 14, 2022, doi: 10.3390/app12147092.
Q. H. Nguyen et al., “Deep Learning Models for Tuberculosis Detection from Chest X-ray Images,” 2019 26th Int. Conf. Telecommun. ICT 2019, pp. 381–385, 2019, doi: 10.1109/ICT.2019.8798798.
N. F. Aurna, M. A. Yousuf, K. A. Taher, A. K. M. Azad, and M. A. Moni, “A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models,” Comput. Biol. Med., vol. 146, no. April, p. 105539, 2022, doi: 10.1016/j.compbiomed.2022.105539.
M. Rahman, Y. Cao, X. Sun, B. Li, and Y. Hao, “Deep pre-trained networks as a feature extractor with XGBoost to detect tuberculosis from chest X-ray,” Comput. Electr. Eng., vol. 93, no. April, p. 107252, 2021, doi: 10.1016/j.compeleceng.2021.107252.
B. Herman, W. Sirichokchatchawan, S. Pongpanich, and C. Nantasenamat, “Development and performance of CUHASROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia,” PLoS One, vol. 16, no. 3 March, pp. 1–19, 2021, doi: 10.1371/journal.pone.0249243.
A. Abid, A. Abdalla, A. Abid, D. Khan, A. Alfozan, and J. Zou, “Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild,” no. Ml, 2019, [Online]. Available: http://arxiv.org/abs/1906.02569
D. Barušauskas, “Tuberculosis Chest X-rays (Shenzhen),” Kaggle, 2020. https://www.kaggle.com/datasets/raddar/tuberculosis-chest-xrays-shenzhen
M. E. H. Chowdhury et al., “Can AI Help in Screening Viral and COVID-19 Pneumonia?,” IEEE Access, vol. 8, pp. 132665–132676, 2020, doi: 10.1109/ACCESS.2020.3010287.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
C. Liu et al., “TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network,” Proc. - Int. Conf. Image Process. ICIP, vol. 2017-Septe, pp. 2314–2318, 2018, doi: 10.1109/ICIP.2017.8296695.
M. Toğaçar, B. Ergen, and Z. Cömert, “Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 23–39, 2020, doi: 10.1016/j.bbe.2019.11.004.
Refbacks
- There are currently no refbacks.
SINERGI
Published by:
Fakultas Teknik Universitas Mercu Buana
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Tlp./Fax: +62215871335
p-ISSN: 1410-2331
e-ISSN: 2460-1217
Journal URL: http://publikasi.mercubuana.ac.id/index.php/sinergi
Journal DOI: 10.22441/sinergi
Journal by SINERGI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
The Journal is Indexed and Journal List Title by:
