Lung X-ray Image Classification for Distinguishing Tuberculosis and Pneumonia Using Pretrained CNN Feature Extractors and Supervised Classifiers
DOI:
https://doi.org/10.63158/journalisi.v8i3.1595Keywords:
tuberculosis, pneumonia, chest radiography, transfer learning, CNN feature extraction, supervised classificationAbstract
Tuberculosis (TB) and pneumonia (PNA) are infectious lung diseases with overlapping chest X-ray (CXR) manifestations, making automated differential classification clinically important and methodologically challenging. This study proposes a supervised CXR classification workflow to distinguish TB from PNA using pretrained convolutional neural network (CNN) feature extractors and supervised classifiers. A publicly available de-identified dataset comprising 390 TB and 390 PNA images was used. Images were screened to exclude duplicates, corrupted files, non-CXR images, unclear labels, and identifiable cases. Preprocessing included format standardization, resizing according to CNN input requirements, and normalization. To reduce augmentation-based leakage risk, no heavy pre-validation augmentation was applied. Image embeddings were extracted using VGG-16, Inception V3, and VGG-19, then classified using Logistic Regression, Support Vector Machine, and Neural Network models. Performance was evaluated using stratified 5-fold cross-validation with AUC, accuracy, F1-score, precision, recall, MCC, and confusion matrix analysis. The Inception V3–Logistic Regression combination achieved the best performance, with AUC of 0.999, accuracy of 0.992, F1-score of 0.992, and MCC of 0.985.
Downloads
References
[1] N. Oktavia, B. S. Miranda, and D. I. Swasono, “CNN-Based Classification of Infectious Lung Diseases using Thorax X-Ray Analysis,” Engineering and Technology Journal, vol. 09, no. 10, Oct. 2024, doi: 10.47191/etj/v9i10.14.
[2] S. K. Mohapatra, M. Abebe, L. Mekuanint, S. Prasad, P. K. Bala, and S. K. Dhala, “Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques,” Review of Computer Engineering Research, vol. 10, no. 4, pp. 136–149, Nov. 2023, doi: 10.18488/76.v10i4.3533.
[3] B. U. Maheswari et al., “Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs,” BMC Med. Imaging, vol. 24, no. 1, p. 32, Feb. 2024, doi: 10.1186/s12880-024-01202-x.
[4] K. Guo, J. Cheng, K. Li, L. Wang, Y. Lv, and D. Cao, “Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study,” BMC Med. Imaging, vol. 23, no. 1, p. 209, Dec. 2023, doi: 10.1186/s12880-023-01174-4.
[5] L. Venkataramana, D. V. V. Prasad, S. Saraswathi, C. M. Mithumary, R. Karthikeyan, and N. Monika, “Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques,” Med. Biol. Eng. Comput., vol. 60, no. 9, pp. 2681–2691, Sep. 2022, doi: 10.1007/s11517-022-02632-x.
[6] T. Xu and Z. Yuan, “Convolution Neural Network With Coordinate Attention for the Automatic Detection of Pulmonary Tuberculosis Images on Chest X-Rays,” IEEE Access, vol. 10, pp. 86710–86717, 2022, doi: 10.1109/ACCESS.2022.3199419.
[7] W. Khan, N. Zaki, and L. Ali, “Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 51747–51771, 2021, doi: 10.1109/ACCESS.2021.3069937.
[8] S.-L. Yi, S.-L. Qin, F.-R. She, and T.-W. Wang, “RED-CNN: The Multi-Classification Network for Pulmonary Diseases,” Electronics (Basel)., vol. 11, no. 18, p. 2896, Sep. 2022, doi: 10.3390/electronics11182896.
[9] Y. Xie et al., “Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs,” J. Healthc. Eng., vol. 2020, pp. 1–12, Aug. 2020, doi: 10.1155/2020/9205082.
[10] Y.-X. Tang et al., “Automated abnormality classification of chest radiographs using deep convolutional neural networks,” NPJ Digit. Med., vol. 3, no. 1, p. 70, May 2020, doi: 10.1038/s41746-020-0273-z.
[11] E. Showkatian, M. Salehi, H. Ghaffari, R. Reiazi, and N. Sadighi, “Deep learning-based automatic detection of tuberculosis disease in chest X-ray images,” Pol. J. Radiol., vol. 87, pp. 118–124, Feb. 2022, doi: 10.5114/pjr.2022.113435.
[12] R. Kundu, R. Das, Z. W. Geem, G.-T. Han, and R. Sarkar, “Pneumonia detection in chest X-ray images using an ensemble of deep learning models,” PLoS One, vol. 16, no. 9, p. e0256630, Sep. 2021, doi: 10.1371/journal.pone.0256630.
[13] T. H. Mandeel, S. M. Awad, and S. Naji, “Pneumonia binary classification using multi-scale feature classification network on chest x-ray images,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 4, p. 1469, Dec. 2022, doi: 10.11591/ijai.v11.i4.pp1469-1477.
[14] A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images,” Comput. Methods Programs Biomed., vol. 196, p. 105581, Nov. 2020, doi: 10.1016/j.cmpb.2020.105581.
[15] A. Abbas, M. M. Abdelsamea, and M. M. Gaber, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network,” Applied Intelligence, vol. 51, no. 2, pp. 854–864, Feb. 2021, doi: 10.1007/s10489-020-01829-7.
[16] A. Harshavardhan, S. Cheerla, A. Parkavi, S. A. Latha Mary, K. Qureshi, and H. R. Mhaske, “Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification,” J. Electron. Imaging, vol. 32, no. 06, May 2023, doi: 10.1117/1.JEI.32.6.062603.
[17] D. M. Ibrahim, N. M. Elshennawy, and A. M. Sarhan, “Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases,” Comput. Biol. Med., vol. 132, p. 104348, May 2021, doi: 10.1016/j.compbiomed.2021.104348.
[18] D. Z. K. G. M. Kermany, “Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images,” Mendeley Data.
[19] S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, “Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.,” Quant. Imaging Med. Surg., vol. 4, no. 6, pp. 475–7, Dec. 2014, doi: 10.3978/j.issn.2223-4292.2014.11.20.
[20] S. Urooj, S. Suchitra, L. Krishnasamy, N. Sharma, and N. Pathak, “Stochastic Learning-Based Artificial Neural Network Model for an Automatic Tuberculosis Detection System Using Chest X-Ray Images,” IEEE Access, vol. 10, pp. 103632–103643, 2022, doi: 10.1109/ACCESS.2022.3208882.
[21] W. Zhang, H. Wang, Z. Lai, and C. Hou, “Constrained Contrastive Representation: Classification On Chest X-Rays With Limited Data,” in 2021 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Jul. 2021, pp. 1–6. doi: 10.1109/ICME51207.2021.9428273.
[22] N. Habib, Md. M. Hasan, Md. M. Reza, and M. M. Rahman, “Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection,” SN Comput. Sci., vol. 1, no. 6, p. 359, Nov. 2020, doi: 10.1007/s42979-020-00373-y.
[23] T. Rahman et al., “Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray,” Applied Sciences, vol. 10, no. 9, p. 3233, May 2020, doi: 10.3390/app10093233.
[24] O. A. Fagbuagun, O. Nwankwo, S. A. Akinpelu, and O. Folorunsho, “Model development for pneumonia detection from chest radiograph using transfer learning,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 20, no. 3, p. 544, Jun. 2022, doi: 10.12928/telkomnika.v20i3.23296.
[25] R. Wajgi et al., “Optimized tuberculosis classification system for chest X‐ray images: Fusing hyperparameter tuning with transfer learning approaches,” Engineering Reports, vol. 6, no. 11, Nov. 2024, doi: 10.1002/eng2.12906.
[26] J. Luján-García, C. Yáñez-Márquez, Y. Villuendas-Rey, and O. Camacho-Nieto, “A Transfer Learning Method for Pneumonia Classification and Visualization,” Applied Sciences, vol. 10, no. 8, p. 2908, Apr. 2020, doi: 10.3390/app10082908.
[27] 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.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
