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Tuberculosis detection using convolutional neural network
Corresponding Author(s) : Mochamad Imron
International Journal of Allied Medical Sciences and Clinical Research,
Vol. 8 No. 3 (2020): 2020 Volume - 8 Issue-3
Abstract
Background
Pulmonary tuberculosis is an infectious disease has become one of the ten leading causes of death globally. Increasing the number and variety of radiological examinations increases the workload of radiologists. This causes the radiologist to experience fatigue, and trigger an inaccurate diagnosis, missed or delayed diagnosis. Machine learning is a computational model with an algorithm that is similar to the structure and function of the biological network of the human brain. It's part of artificial intelligence that uses computer science to perform digital image processing with pattern recognition techniques. The algorithm in machine learning can calculate, recognize the pattern in the image, and make predictive diagnoses.
Objective
Generate deep learning model that can classify the chest x-rays image as tuberculosis and normal, also have the same performance with radiologists.
Methods
The deep learning model using Convolutional Neural Network (CNN) with the input image size and filter size variation has developed, then compared to the expert performance.
Results
Obtained the optimum deep learning model using an image of 200 x 200 and 5 x 5 filter size that has an accuracy, sensitivity, specificity, precision, and AUC were 0.97, 0.9667, 0.975, 0.9831, and 0.971 with CI of 0.932-1.
Conclusion
The deep learning model has 98% classification similarity with expert has obtained.
Keywords
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[17]. Huang J, Ling CX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng.17(3), 2005, 299–310.
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Available from: http://dx.doi.org/10.1053/j.semnuclmed.2011.06.005
References
bacterium. J Med Microbiol. 64(11), 2015, 1261–9.
[2]. Magnabosco GT, Lopes LM, Andrade RL de P, Brunello MEF, Monroe AA, Villa TCS. Tuberculosis Control in
People Living With HIV/AIDS. Rev Lat Am Enfermagem. 24(e2798), 2016, 1–8.
[3]. Anderson L, Baddeley A, Monica Dias H, Floyd K, Baena IG, Gebreselassei N, Global Tuberculosis Report.
Geneva: World Health Organization; 2018.
[4]. Kowalczyk N. Radiologic Pathology for Technologists. Sixth Edit. Ohio: Elsevier Mosby; 2014, 472.
[5]. Reiner BI, Krupinski E. The insidious problem of fatigue in medical imaging practice. J Digit Imaging. 25(1),
2012, 3–6.
[6]. Muenzel D, Engels HP, Bruegel M, Kehl V, Rummeny EJ, Metz S. Intra- and inter-observer variability in
measurement of target lesions: Implication on response evaluation according to RECIST 1.1. Radiol Oncol. 46(1),
2012, 8–18.
[7]. Norweck JT, Seibert JA, Andriole KP, Clunie DA, Curran BH, Flynn MJ, ACR-AAPM-SIIM technical standard
for electronic practice of medical imaging. J Digit Imaging. 26(1), 2013, 38–52.
[8]. Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional Neural Networks for
Radiologic Images: A Radiologist’s Guide. Radiology. 290(3), 2019, 590–606.
[9]. Sar?gül M, Ozyildirim BM, Avci M. Differential convolutional neural network. Neural Networks. 116, 2019, 279–
87.
[10]. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Deep learning in medical imaging: General overview. Korean
J Radiol. 18(4), 2017, 570–84.
[11]. Steingart KR, Schiller I, Horne DJ, Pai M, Boehme CC, Dendukuri N. Xpert ® MTB / RIF assay for pulmonary
tuberculosis and rifampicin resistance in adults ( Review ) Xpert ® MTB / RIF assay for pulmonary tuberculosis
and rifampicin resistance in adults. Cochrane Libr. (1), 2014, 1–3.
[12]. Tuberculosis Coalition for Technical Assistance. Handbook for Using International Standar for Tuberculosis
Care. USAID, editor. World Health Organization; 2007.
[13]. Pianykh OS. Digital Imaging and Communications in Medicine (DICOM). Second Edi. New York: Springer;
2012, 23.
[14]. Bruce W, Rollins J. Merrill ’ S Atlas of Radiographic Positioning & Procedures. Thirteenth. St. Louis: Elsevier
Mosby; 2016.
[15]. Raschka S. Python Machine Learning. Birmingham: Packt Publishing Ltd; 2016, 425.
[16]. McHugh ML. Lessons in biostatistics interrater reliability : the kappa statistic. Biochem Medica. 22(3), 2012,
276–82.
[17]. Huang J, Ling CX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng.17(3), 2005, 299–310.
[18]. Hajian K. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Casp
J Intern Med. 4(2), 2013, 627–35.
[19]. Burgess AE. Visual perception studies and observer models in medical imaging. Semin Nucl Med. 41(6), 2011,
419–36.
Available from: http://dx.doi.org/10.1053/j.semnuclmed.2011.06.005
