Tuberculosis Diagnosis From X-Ray Images Using Deep Learning And Contrast Enhancement Techniques
DOI:
https://doi.org/10.52436/1.jutif.2025.6.2.4315Keywords:
Chest X-ray, CLAHE, CNN, DenseNet201, TuberculosisAbstract
Tuberculosis (TB) is an infectious disease that poses a global health threat. Early diagnosis through chest X-ray (CXR) imaging is effective in reducing transmission and improving patient recovery rates. However, the limited number of radiologists in high TB burden areas hampers rapid and accurate detection. This study aims to improve TB diagnosis accuracy using deep learning models. Convolutional Neural Networks (CNN) are applied to analyze CXR images to support automated detection in regions with limited radiology personnel. The method involves image processing using Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. A public dataset consisting of 2,188 images was used, with preprocessing steps including resizing, normalization, and augmentation. The DenseNet201 model was employed as the main architecture, trained for 10 epochs with various batch sizes to evaluate its performance. Results show that the combination of CLAHE and DenseNet201 achieved the highest accuracy of 94.84%. Image quality enhancement with CLAHE proved to improve accuracy compared to models without preprocessing. This research contributes to enhancing the efficiency of automated early TB detection, reducing reliance on radiologists, and accelerating clinical decision-making.
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References
S. N. Hankare And S. S. Shirguppikar, “Detection Of Tuberculosis And Lung Cancer Using Cnn,” In Handbook Of Smart Materials, Technologies, And Devices: Applications Of Industry 4.0: Volume 1-3, Vol. 2, 2022. Doi: 10.1007/978-3-030-84205-5_134.
W. Zhou Et Al., “Deep Learning-Based Pulmonary Tuberculosis Automated Detection On Chest Radiography: Large-Scale Independent Testing,” Quant Imaging Med Surg, Vol. 12, No. 4, Pp. 2344–2355, Apr. 2022, Doi: 10.21037/Qims-21-676.
World Health Organization, Regional Strategic Plan Towards Ending Tb In The Who South-East Asia Region : 2021-2021. 2021.
M. Halim, A. Sabrina, And M. Aris Sekolah Tinggi Ilmu Kesehatan Ikifa, “Kepatuhan Pasien Rawat Jalan Poli Paru Dalam Penggunaan Obat Anti Tuberkulosis (Oat) Di Rumah Sakit Kartika Husada Jatiasih Bekasi,” Jurnal Farmasi Ikifa, Vol. 2, No. 1, 2023.
C. Zuo Et Al., “Deep Learning In Optical Metrology: A Review,” 2022. Doi: 10.1038/S41377-022-00714-X.
L. Mohammadpour, T. C. Ling, C. S. Liew, And A. Aryanfar, “A Survey Of Cnn-Based Network Intrusion Detection,” 2022. Doi: 10.3390/App12168162.
D. Alsaeed And S. F. Omar, “Brain Mri Analysis For Alzheimer’s Disease Diagnosis Using Cnn-Based Feature Extraction And Machine Learning,” Sensors, Vol. 22, No. 8, 2022, Doi: 10.3390/S22082911.
B. Tugrul, E. Elfatimi, And R. Eryigit, “Convolutional Neural Networks In Detection Of Plant Leaf Diseases: A Review,” 2022. Doi: 10.3390/Agriculture12081192.
T. T. Alemayoh, M. Shintani, J. H. Lee, And S. Okamoto, “Deep-Learning-Based Character Recognition From Handwriting Motion Data Captured Using Imu And Force Sensors,” Sensors, Vol. 22, No. 20, 2022, Doi: 10.3390/S22207840.
J. Qiang, D. Wu, H. Du, H. Zhu, S. Chen, And H. Pan, “Review On Facial-Recognition-Based Applications In Disease Diagnosis,” 2022. Doi: 10.3390/Bioengineering9070273.
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, No. 1, 2022, Doi: 10.5114/Pjr.2022.113435.
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.
M. Nijiati Et Al., “Artificial Intelligence Assisting The Early Detection Of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study,” Front Mol Biosci, Vol. 9, Apr. 2022, Doi: 10.3389/Fmolb.2022.874475.
M. Oloko-Oba And S. Viriri, “Ensemble Of Efficientnets For The Diagnosis Of Tuberculosis,” Comput Intell Neurosci, Vol. 2021, 2021, Doi: 10.1155/2021/9790894.
S. Saifullah And R. Drezewski, “Modified Histogram Equalization For Improved Cnn Medical Image Segmentation,” In Procedia Computer Science, 2023. Doi: 10.1016/J.Procs.2023.10.295.
M. Zheng And W. Luo, “Underwater Image Enhancement Using Improved Cnn Based Defogging,” Electronics (Switzerland), Vol. 11, No. 1, 2022, Doi: 10.3390/Electronics11010150.
S. Roy, M. Tyagi, V. Bansal, And V. Jain, “Svd-Clahe Boosting And Balanced Loss Function For Covid-19 Detection From An Imbalanced Chest X-Ray Dataset,” Comput Biol Med, Vol. 150, 2022, Doi: 10.1016/J.Compbiomed.2022.106092.
S. A. Widiarto, W. A. Saputra, And A. R. Dewi, “Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia),” Jipi (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), Vol. 6, No. 2, 2021, Doi: 10.29100/Jipi.V6i2.2102.
R. K. Hapsari, M. I. Utoyo, R. Rulaningtyas, And H. Suprajitno, “Comparison Of Histogram Based Image Enhancement Methods On Iris Images,” In Journal Of Physics: Conference Series, 2020. Doi: 10.1088/1742-6596/1569/2/022002.
Erwin And D. R. Ningsih, “Improving Retinal Image Quality Using The Contrast Stretching, Histogram Equalization, And Clahe Methods With Median Filters,” International Journal Of Image, Graphics And Signal Processing, Vol. 12, No. 2, 2020, Doi: 10.5815/Ijigsp.2020.02.04.
S. Aulia And D. Rahmat, “Brain Tumor Identification Based On Vgg-16 Architecture And Clahe Method,” International Journal On Informatics Visualization, Vol. 6, No. 1, 2022, Doi: 10.30630/Joiv.6.1.864.
Kamal And H. Ez-Zahraouy, “A Comparison Between The Vgg16 , Vgg19 And Resnet50 Architecture Frameworks For Classi Cation Of Normal And Clahe Processed Medical Images,” 2023.
R. M. Diar, R. Y. N. Fu’adah, And K. Usman, “Klasifikasi Penyakit Paru-Paru Berbasis Pengolahan Citra X Ray Menggunakan Convolutional Neural Network (Classification Of The Lung Diseases Based On X Ray Image Processing Using Convolutional Neural Network),” E-Proceeding Of Engineering, Vol. 9, No. 2, 2022.
A. Alhudhaif, K. Polat, And O. Karaman, “Determination Of Covid-19 Pneumonia Based On Generalized Convolutional Neural Network Model From Chest X-Ray Images,” Expert Syst Appl, Vol. 180, Oct. 2021, Doi: 10.1016/J.Eswa.2021.115141.
F. Hussein Et Al., “Hybrid Clahe-Cnn Deep Neural Networks For Classifying Lung Diseases From X-Ray Acquisitions,” Electronics (Switzerland), Vol. 11, No. 19, 2022, Doi: 10.3390/Electronics11193075.
S. S. M. Sheet, T. S. Tan, M. A. As’ari, W. H. W. Hitam, And J. S. Y. Sia, “Retinal Disease Identification Using Upgraded Clahe Filter And Transfer Convolution Neural Network,” Ict Express, Vol. 8, No. 1, 2022, Doi: 10.1016/J.Icte.2021.05.002.
R. Fan, X. Li, S. Lee, T. Li, And H. L. Zhang, “Smart Image Enhancement Using Clahe Based On An F-Shift Transformation During Decompression,” Electronics (Switzerland), Vol. 9, No. 9, 2020, Doi: 10.3390/Electronics9091374.
M. B. Kurniwan And E. Utami, “Comparative Analysis Of Contrast Enhancement Methods For Classification Of Pekalongan Batik Motifs Using Convolutional Neural Network,” Jurnal Teknik Informatika (Jutif), Vol. 5, No. 6, Pp. 1779–1787, 2024, Doi: 10.52436/1.Jutif.2024.5.6.2621.
T. Mao And D. X. Zhou, “Rates Of Approximation By Relu Shallow Neural Networks,” J Complex, Vol. 79, 2023, Doi: 10.1016/J.Jco.2023.101784.
A. D. Saputra, D. Hindarto, And H. Santoso, “Disease Classification On Rice Leaves Using Densenet121, Densenet169, Densenet201,” Sinkron, Vol. 8, No. 1, 2023, Doi: 10.33395/Sinkron.V8i1.11906.
S. Majumdar, P. Pramanik, And R. Sarkar, “Gamma Function Based Ensemble Of Cnn Models For Breast Cancer Detection In Histopathology Images,” Expert Syst Appl, Vol. 213, 2023, Doi: 10.1016/J.Eswa.2022.119022.
S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, And D. Traore, “Deep Convolution Neural Network Sharing For The Multi-Label Images Classification,” Machine Learning With Applications, Vol. 10, 2022, Doi: 10.1016/J.Mlwa.2022.100422.
P. Ghose, M. A. Uddin, U. K. Acharjee, And S. Sharmin, “Deep Viewing For The Identification Of Covid-19 Infection Status From Chest X-Ray Image Using Cnn Based Architecture,” Intelligent Systems With Applications, Vol. 16, 2022, Doi: 10.1016/J.Iswa.2022.200130.
Ismai, “Machine Learning: Teori, Studi Kasus, Dan Implementasi Menggunakan Phyton,” 2021.
A. Sayeed Et Al., “An Effective Screening Of Covid-19 Pneumonia By Employing Chest X-Ray Segmentation And Attention-Based Ensembled Classification,” Iet Image Process, Vol. 18, No. 9, Pp. 2400–2416, Jul. 2024, Doi: 10.1049/Ipr2.13106.
H. A. Sanghvi, R. H. Patel, A. Agarwal, S. Gupta, V. Sawhney, And A. S. Pandya, “A Deep Learning Approach For Classification Of Covid And Pneumonia Using Densenet-201,” Int J Imaging Syst Technol, Vol. 33, No. 1, 2023, Doi: 10.1002/Ima.22812.
M. Zia Ur Rehman, F. Ahmed, S. A. Alsuhibany, S. S. Jamal, M. Zulfiqar Ali, And J. Ahmad, “Classification Of Skin Cancer Lesions Using Explainable Deep Learning,” Sensors, Vol. 22, No. 18, 2022, Doi: 10.3390/S22186915.
A. Wanto, Y. Yuhandri, And O. Okfalisa, “Optimization Accuracy Of Cnn Model By Utilizing Clahe Parameters In Image Classification Problems,” In Proceedings - 2023 International Conference On Networking, Electrical Engineering, Computer Science, And Technology, Iconnect 2023, 2023. Doi: 10.1109/Iconnect56593.2023.10327100.
S. Hadiyoso, S. Aulia, And I. D. Irawati, “Diagnosis Of Lung And Colon Cancer Based On Clinical Pathology Images Using Convolutional Neural Network And Clahe Framework,” International Journal Of Applied Science And Engineering, Vol. 20, No. 1, 2023, Doi: 10.6703/Ijase.202303_20(1).006.
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