Revolution in Detecting Tuberculosis using Radiology with Application of Deep Learning Algorithm
DOI:
https://doi.org/10.55175/cdk.v48i4.70Kata Kunci:
Deep learning, early detection, radiology, tuberculosisAbstrak
Radiology is a medical examination of internal body parts using data imaging to interpret an illness. Many illnesses can be detected using this medical discipline; one of the diseases is tuberculosis caused by Mycobacterium tuberculosis bacteria. The supreme ability of Artificial Intelligence and Machine learning has amazed the radiologist in analyzing big data-based information. A better deep learning algorithm can lead radiologist to accurate results. This article will review ten (10) research papers that use a deep learning algorithm in the application to detect tuberculosis by data processing technique. The goal is to know the best type of data processing in deep learning to detect TB.
Radiologi adalah pemeriksaan bagian dalam tubuh menggunakan data pencitraan untuk interpretasi suatu penyakit. Banyak penyakit dapat dideteksi menggunakan disiplin medis ini; salah satunya adalah tuberkulosis yang disebabkan oleh bakteri Mycobacterium tuberculosis yang menyerang paru. Ahli radiologi tertarik atas kemampuan Artificial Intelligence dan Machine Learning untuk analisis data yang akurat. Artikel ini akan mengulas sepuluh (10) makalah penelitian aplikasi algoritma deep learning untuk deteksi tuberkulosis menggunakan teknik pengolahan data
Unduhan
Referensi
McBee MP, Awan OA, Colucci AT, Ghobadi CW, Kadom N, Kansagra AP, et al. Deep learning in radiology. Acad Radiol. 2018;25(11):1472-80.
Parikesit AA, Agustriawan D, Nurdiansyah R. Protein annotation of breast-cancer-related proteins with machine-learning tools. Makara J Sci. 2020;24(2):6.
Parikesit AA, Nurdiansyah R, Agustriawan D. Penerapan pendekatan machine learning pada pengembangan basis data herbal sebagai sumber informasi kandidat obat kanker. J Agroindustrial Technol [Internet]. 2019 Oct 21. Available from: http://journal.ipb.ac.id/index.php/jurnaltin/article/view/27931[cited 2019 Oct 23];29(2).
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.
Copeland M. The difference between AI, machine learning, and deep learning?. NVIDIA Blog [Internet]. 2016 [cited 2020 Aug 27];1–5. Available from: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: A primer for radiologists. RadioGraphics. 2017;37(7):2113–31.
Monshi MMA, Poon J, Chung V. Deep learning in generating radiology reports: A survey. Artif Intell Med. 2020;106:101878.
Harries AD, Dye C. Tuberculosis. Ann Trop Med Parasitol. 2006;100(5–6):415–31.
Zaman K. Tuberculosis: A global health problem. J Heal Popul Nutr. 2010;28(2):111–3.
World Health Organisation. World TB day 2020 [Internet]. 2020 [cited 2020 Aug 27]. Available from: https://www.who.int/campaigns/world-tb-day/world-tbday-2020
Hooda R, Sofat S, Kaur S, Mittal A, Meriaudeau F. Deep-learning: A potential method for tuberculosis detection using chest radiography. In: 2017 IEEE Internat Conf on signal and image processing Applications (ICSIPA) [Internet]. 2017:497–502. Available from: https://ieeexplore.ieee.org/document/8120663/
Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284(2):574–82.
Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, et al. Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clin Infect Dis. 2019 ;69(5):739–47.
Becker AS, Blüthgen C, Phi van VD, Sekaggya-Wiltshire C, Castelnuovo B, Kambugu A, et al. Detection of tuberculosis patterns in digital photographs of chest X-ray images using deep learning: Feasibility study. Int J Tuberc Lung Dis. 2018;22(3):328–35.
Alcantara MF, Cao Y, Liu C, Liu B, Brunette M, Zhang N, et al. Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor communities in Perú. Smart Heal. 2017;1–2:66–76.
Stirenko S, Kochura Y, Alienin O, Rokovyi O, Gordienko Y, Gang P, et al. Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation. In: 2018 IEEE 38th Internat Conf on Electronics and Nanotechnology (ELNANO) [Internet]. 2018:422–8. Available from: https://ieeexplore.ieee.org/document/8477564/
Singh R, Kalra MK, Nitiwarangkul C, Patti JA, Homayounieh F, Padole A, et al. Deep learning in chest radiography: Detection of findings and presence of change.Eapen GA, ed. PLoS One. 2018;13(10):e0204155.
Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Sheikh A, ed. PLoS Med [Internet]. 2018;15(11):e1002686. Available from: https://dx.plos.org/10.1371/journal.pmed.1002686
Ting DSW, Yi PH, Hui F. Clinical applicability of deep learning system in detecting tuberculosis with chest radiography. Radiology 2018;286(2):729–31.
Heo SJ, Kim Y, Yun S, Lim SS, Kim J, Nam CM, et al. Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data. Int J Environ Res Public Health [Internet]. 2019;16(2):250.
Koziarski M, Cyganek B. Image recognition with deep neural networks in presence of noise - Dealing with and taking advantage of distortions. Integr Comput Aided Eng. 2017;24(4):337–49.
Classification: ROC Curve and AUC | Machine Learning Crash Course [Internet]. developers.google.com. 2020 [cited 2020 Aug 27:1. Available from: https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc
Sabottke CF, Spieler BM. The effect of image resolution on deep learning in radiography. Radiol Artif Intell. 2020;2(1):e190015.
Yi PH, Kim TK, Lin CT. Generalizability of deep learning tuberculosis classifier to COVID-19 chest radiographs. J Thorac Imaging [Internet]. 2020;35(4):102–4.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2021 Cermin Dunia Kedokteran
Artikel ini berlisensi Creative Commons Attribution-NonCommercial 4.0 International License.