Preliminary Screening for Pulmonary Tuberculosis from Chest Radiography using Artificial Neural Network

Preliminary Screening for Pulmonary Tuberculosis from Chest Radiography using Artificial Neural Network

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© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Sucheera Phramala, Weeragul Pratumgul, Jagraphon obma, Worawat Sa-ngiamvibool
DOI : 10.14445/22315381/IJETT-V70I8P233

How to Cite?

Sucheera Phramala, Weeragul Pratumgul, Jagraphon obma, Worawat Sa-ngiamvibool, "Preliminary Screening for Pulmonary Tuberculosis from Chest Radiography using Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 318-326, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P233

Abstract
This research has developed an algorithm for primary screening of pulmonary tuberculosis from chest radiographs by using the image processing principle with Artificial Neural Network (ANN) to examine the preliminary features related to the high incidence of pulmonary tuberculosis, namely Reticular Infiltration, Cavity, and Consolidation. The procedure was then used to learn 14,000 chest radiographs and tested for preliminary screening on 6,000 test images. The test result found that this method can process with an accuracy of 82.20%, a sensitivity of 86.80%, specificity of 79.59% and positive predictive values of 77.37% compared with the radiologist.

Keywords
Chest radiography, Artificial neural network, Image processing, Pulmonary tuberculosis.

Reference
[1] World Health Organization, Global Tuberculosis Control: WHO Report, 2012, [Online]. Available: http://www.who.int/tb/publications/global_report/2012/gtbr12_full.pdf
[2] Tuberculosis Office, Department of Disease Control, Ministry of Public Health, THAILAND, “Tuberculosis Data Center.TBcm(Data Center,” 2021. [Online]. Available: http://122.155.219.72/tbdc/frontend/web/index.php.
[3] Kanchana Chansung, “Critical Appraisal of Scientific Paper(Part III),” Srinagarind Med J, vol. 14, no. 1, pp. 62–67, 1999.
[4] Sant Anna CC, Schmidt CM, Pombo March MF, Pereira SM, Barreto ML, “Radiologic Findings of Pulmonary Tuberculosis in Adolescents,” Braz J Infect Dis, vol. 15, no. 1, pp. 40–44, 2014.
[5] Tempon Kuamak, Ravivan Pantaverakulans Pasuporn Pho-ngennark,“Chest Radiographic Findings in Pulmonary Tuberculosis,” Buddha Chinaraj Medical Journal, vol. 32, no. 2, pp. 134–141, 2016.
[6] Wasinan Pholphuech, “Chest Radiographic Finding in Pulmonary Tuberculosis (Banmee Hospital),” Research and Development Health System Journal, vol. 13, no. 2, pp. 11–19, 2020.
[7] A.Fojnica, A.Osmanovic and A. Badnjevic, “Dynamic Model of Tuberculosis-Multiple Strain Prediction Based on Artificial Neural Network,” In 5th Mediterranean Conference on Embedded Computing, pp. 290–293, 2016.
[8] S.Candemir, S.Jaeger, K.Palaniappa, J.P.Musco, R.K.Singh, Z.Xue, A.Karargyris, S.Antani, G.Thoma, and C.J.McDonald, “Lung Segmentation in Chest Radiographs Using Anatomical Atlases with Nonrigid Registeration,” IEEE Transaction on Medical Imaging, vol. 3, no. 2, pp. 577–590, 2014.
[9] H.Das and A.Nath, “An Efficient Detection of Tuberculosis from Chest X-Rays,” International Journal of Advance Research in Computer Science and Management Studies, vol. 3, no. 5, pp. 149–154, 2015.
[10] Cao, Yu et al., “Improving Tuberculosis Diagnostics Using Deep Learning and Mobile Health Technologies among Resource-Poor and Marginalized Communities,” Proceedings - IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE, vol. 2016, no. 1, pp. 274–281, 2016.
[11] S.Hwang, H.Kim, Ji Jeongb, and H.Kimc, “A Novel Approach for Tuberculosis Screening Based on Deep Learning Convolutional Neural Networks,” In SPIE Optical Engineering Press, 2016a.
[12] “A Novel Approach for Tuberculosis Screening Based on Deep Learning Convolutional Neural Networks,” In Proceedings of SPIE, Medical Imaging: Computer-Aided Diagnosis, 2016b.
[13] Liu, Chang et al., “TX-CNN: Detecting Tuberculosis in Chest X-Ray Images Using Convolutional Neural Network,” Proceedings - International Conference on Image Processing, ICIP, pp. 2314–2318, 2018
[14] Tawsifur Rahman, AmithKhandakar, Muhammad Abdul Kadir, Khandaker R. Islam, Khandaker F. Islam, Rashid Mazhar, Tahir Hamid, Mohammad T. Islam, Zaid B. Mahbub, Mohamed ArseleneAyari and Muhammad E. H. Chowdhury, “Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization,” IEEE Access, vol. 8, pp. 191586 – 191601, 2020.
[15] EmanShowkatian, Mohammad Salehi, Hamed Ghaffari, Reza Reiazi,corresponding and Nahid Sadighi, “Deep Learning-Based Automatic Detection of Tuberculosis Disease in Chest X-Ray Images,” Pol J Radiol, vol. 87, pp. 118 – 124, 2022.
[16] P. Seetha Subha Priya , S. Nandhinidevi , M. Thangamani and S. Nallusamy4, “A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis,” International Journal of Engineering Trends and Technology, vol. 68, no. 10, pp. 63–66, 2020.
[17] Yasser Mohammad Al-Sharo, Amer Tahseen Abu-Jassar, SvitlanaSotnik and Vyacheslav Lyashenko, “Neural Networks as a Tool for attern Recognition of Fasteners,” International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 151–160, 2021.
[18] Nafis Khuriyati, Agung Putra Pamungkas, Anggraito Agung P., "The Sorting and Grading of Red Chilli Peppers (Capsicum annuum L.) Using Digital Image Processing," International Journal of Agriculture & Environmental Science, vol. 6, no. 4, pp. 17-23, 2019. Crossref, https://doi.org/10.14445/23942568/IJAES-V6I4P104
[19] Shubham Srivastava, Himanshu Bhardwaj, Aman Dixit, Prof. Namita Kalyan Shinde, "ECG Pattern Analysis Using Artificial Neural Network," International Journal of Electronics and Communication Engineering, vol. 7, no. 5, pp. 1-4, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I5P101.