Classification of Tuberculosis using a Convolutional Neural Network
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Abstract
Tuberculosis disease causes the death of 1.8 million people worldwide, according to data from the World Health Organization in 2018 that nearly 10 million people were affected by tuberculosis and about 98,000 of them died. Technological developments, especially computer engineering, have helped accelerate the diagnosis of tuberculosis in poverty source areas. One of them is Computer Vision and Machine Learning Technology which causes development and utilization in all aspects, including the health sector. In the development of computer vision, there is a deep learning technique, this technique can automatically detect and classify various diseases with better accuracy. One method of deep learning that can produce precise accuracy and better efficiency is using a Convolutional Neural Network (CNN). This study uses CNN in the classification of the Tuberculosis Chest X-ray Database. The study was carried out in 4 scenarios, with each scenario getting accuracy results of 70%, 97%, 97%, and 72%..
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References
W. Supartini and H. Hindarto, “Sistem Pakar Berbasis Web Dengan Metode Forward Chaining Dalam Mendiagnosa Dini Penyakit Tuberkulosis Di Jawa Timur,” Kinetik, vol. 1, no. 3, p. 147, 2016, doi: 10.22219/kinetik.v1i3.123.
O. Rochmawanti, F. Utaminingrum, and F. A. Bachtiar, “Analisis Performa Pre-Trained Model Convolutional Neural Network dalam Mendeteksi Penyakit Tuberkulosis,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 4, p. 805, 2021, doi: 10.25126/jtiik.2021844441.
C. Liu et al., “TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network,” Proc. - Int. Conf. Image Process. ICIP, vol. 2017-Septe, pp. 2314–2318,2018, doi: 10.1109/ICIP.2017.8296695.
N. W. Septarini, “Metode Pengendalian Penyakit Menular,” Fak. Kedokteran, Univ. Udayana, pp. 121–125, 2017.
B. P. Putra, Y. Yunus, and Sumijan, “Sistem Pakar dalam Mendiagnosis Penyakit Mata dengan Menggunakan Metode Forward Chaining,” J. Inf. dan Teknol., vol. 3, pp. 128–133, 2021, doi: 10.37034/jidt.v3i3.122.
A. Ahyuna and K. Aryasa, “Sistem Pakar Diagnosa Dan Tatalaksana Penyakit Demam Berdarah Dengue Menggunakan Jaringan Saraf Tiruan Backpropagation,” E-JURNAL JUSITI J. Sist. Inf. dan Teknol. Inf., vol. 6, no. 1, pp. 1–11, 2017, [Online]. Available: http://ejurnal.dipanegara.ac.id/index.php/jusiti/article/view/40.
R. Rahmadewi and R. Kurnia, “Klasifikasi Penyakit Paru Berdasarkan Citra Rontgen dengan Metoda Segmentasi Sobel,” J. Nas. Tek. Elektro, vol. 5, no. 1, p. 7, 2016, doi: 10.25077/jnte.v5n1.174.2016.
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “ChestX-ray: Hospital- Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases,” Adv. Comput. Vis. Pattern Recognit., pp. 369– 392, 2019, doi: 10.1007/978-3-030-13969-8_18.
D. Litchfield, L. J. Ball, T. Donovan, D. J. Manning, and T. Crawford, “Viewing another person’s eye movements improves identification of pulmonary nodules in chest x-ray inspection,” J. Exp. Psychol. Appl., vol. 16, no. 3, pp. 251–262, 2010, doi: 10.1037/a0020082.
N. Suhermi, S. Suhartono, I. M. G. M. Dana, and D. D. Prastyo, “Pemilihan Arsitektur Terbaik pada Model Deep Learning Melalui Pendekatan Desain Eksperimen untuk Peramalan Deret Waktu Nonlinier,” Stat. J. Theor. Stat. Its Appl., vol. 18, no. 2, pp. 153–159, 2019, doi: 10.29313/jstat.v18i2.4545.
I. M. Baltruschat, H. Nickisch, M. Grass, T. Knopp, and A. Saalbach, “Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification,” Sci. Rep., vol. 9, no. 1, pp. 1–10, 2019, doi: 10.1038/s41598-019-42294-8.
H. Liu, L. Wang, Y. Nan, F. Jin, Q. Wang, and J. Pu, “SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images,” Comput. Med. Imaging Graph., vol. 75, pp. 66–73, 2019, doi: 10.1016/j.compmedimag.2019.05.005.
G. Kim and C. Lee, Convolutional Neural Network 를 이용한 한국어 영화평 감성 분석 Using
Convolutional Neural Network, vol. 2644. Springer International Publishing, 2016.
D. A. V. Dyk and X. L. Meng, “The art of data augmentation,” J. Comput. Graph. Stat., vol. 10, no. 1, pp. 1–50, 2001, doi: 10.1198/10618600152418584.
J. Sanjaya and M. Ayub, “Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 311–323, 2020, doi: 10.28932/jutisi.v6i2.2688.
B. Liu, X. Wang, R. Kwitt, and N. Vasconcelos, “Feature Space Transfer for Data Augmentation Mandar Dixit Microsoft,” Cvpr, pp. 9090–9098, 2018, [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Feature_Space_Transfer_CV PR_2018_paper.pdf.