DOI: https://doi.org/10.14710/jtsiskom.2021.14197

Deteksi osteoporosis pada citra radiograf panoramik dental menggunakan algoritme J48 dan learning vector quantization

Osteoporosis detection on the dental panoramic radiographic images using J48 algorithm and learning vector quantization

Department of Informatics, Universitas Teknologi Yogyakarta. Jl. Siliwangi, Ring Road Utara, Jombor, Sleman, Daerah Istimewa Yogyakarta 55285, Indonesia

Received: 15 Apr 2021; Revised: 20 Jun 2021; Accepted: 11 Jul 2021; Published: 31 Oct 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Osteoporosis is one type of disease that is not easily detected. This disease can cause fractures for the sufferer. Early detection of osteoporosis is crucial to prevent fractures. This study aims to detect osteoporosis through features extracted from cortical bone and trabeculae in dental panoramic images. The results of the selected feature extraction are trained using an artificial neural network. Based on the study results, the dominant features for osteoporosis detection are radio morphometric index and morphological features. The accuracy, sensitivity, and specificity of the J48 and Learning Vector Quantization (LVQ) are 83.88 %, 78.57 %, and 100 %, respectively.
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Keywords: osteoporosis; dental panoramic; radio morphometry index; texture analyis; J48; LVQ
Funding: Universitas Teknologi Yogyakarta

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Section: Original Research Articles
Language : ID
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  1. J.-S. Lee, S. Adhikari, L. Liu, H.-G. Jeong, H. Kim, and S.-J. Yoon, “Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study,” Dentomaxillofacial Radiology, vol. 48, 20170344, 2018. doi: 10.1259/dmfr.20170344
  2. E. I. Sela and R. Pulungan, “Osteoporosis identification based on the validated trabecular area on digital dental radiographic images,” Procedia Computer Science, vol. 157, pp. 2820289, 2019. doi: 10.1016/j.procs.2019.08.168
  3. E. I. Sela, R. Pulungan, R. Widyaningrum, and R. R. Shantiningsih, “Method for automated selection of the trabecular area in digital periapical radiographic images using morphological operations,” Healthcare Informatics Research, vol. 25, no. 3, pp. 193-200, 2019. doi: 10.4258/hir.2019.25.3.193
  4. R. Widyaningrum, S. Lestari, and F. Jie, “Image analysis of periapical radiograph for bone mineral density prediction,” International Journal of Electrical and Computer Engineering, vol. 8, no. 4, pp. 2083–2090, 2018. doi: 10.11591/ijece.v8i4.pp2083-2090
  5. B. Popić et al., “The Radiomorphometric Indices of the Mandible as a Screening Method for Early Detection of Osteoporosis in Postmenopausal Women,” Collegium antropologicum, vol. 45, no. 1, pp. 31–37, 2021. doi: 10.5671/ca.45.1.4
  6. J. J. Hwang et al., “Strut analysis for osteoporosis detection model using dental panoramic radiography,” Dentomaxillofacial Radiology, vol. 46, 20170006, 2017. doi: 10.1259/dmfr.20170006
  7. E. Güngör, D. Yildirim, and R. Çevik, “Evaluation of osteoporosis in jaw bones using cone beam CT and dual-energy X-ray absorptiometry,” Journal of Oral Science, vol. 58, no. 2, pp. 185–194, 2016. doi: 10.2334/josnusd.15-0609
  8. L. Khojastepour, M. Hasani, M. Ghasemi, A. Mehdizadeh, and F. Tajeripour, “Mandibular trabecular bone analysis using local binary pattern for osteoporosis diagnosis,” Journal of Biomedical Physics and Engineering, vol. 9, no. 1, pp. 81–88, 2019. doi: 10.31661/jbpe.v9i1Feb.743
  9. R. Widyaningrum, N. Kertia, and A. Harjoko, “The relationship between bone mass density and radiomorphometric index on menopausal women from Javanese ethnic in Indonesia: a pilot study,” in the International Conference on Biomedical Engineering and Medical Applications, Yogyakarta, Indonesai, Jan. 2012, pp. 96–99
  10. R. Widyaningrum and S. Lestari, “The correlation between mandible trabecular texture parameter on panoramic radiograph with bone mass density,” in the International Symphosium Advanced Clinical Approach for the Prevention of Dental Caries and Implicated Diseases, Yogyakarta, Indonesia, Nov. 2013, pp. 17–19
  11. E. I. Sela, S. Hartati, A. Harjoko, R. Wardoyo, and M. Mudjosemedi, “Feature selection of the combination of porous trabecular with anthropometric features for osteoporosis screening,” International Journal of Electrical and Computer Engineering, vol. 5, no. 1, pp. 78-83, 2015. doi: 10.11591/ijece.v5i1.pp78-83
  12. R. A. Syifa, K. Adi, and C. E. Widodo, “Analisis tekstur citra mikroskopis kanker paru menggunakan metode gray level co-occurance matrix (GLCM) dan tranformasi wavelet dengan klasifikasi Naive Bayes,” Youngster Physics Journal, vol. 5, no. 4, pp. 457–462, 2016
  13. F. J. Kaunang, “Penerapan algoritma J48 decision tree untuk analisis tingkat kemiskinan di Indonesia,” CogITo Smart Journal, vol. 4, no. 2, pp. 348-357, 2019. doi: 10.31154/cogito.v4i2.141.348-357
  14. R. Hamidi, M. T. Furqon, and B. Rahayudi, “Implementasi learning vector quantization (LVQ) untuk klasifikasi kualitas air sungai,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 12, pp. 1758–1763, 2017
  15. K.-S. Lee, S.-K. Jung, J.-J. Ryu, S.-W. Shin, and J. Choi, “Evaluation of Transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs,” Journal of Clinical Medicine, vol. 9, no. 2, 392, 2020. doi: 10.3390/jcm9020392
  16. C. Muramatsu et al., “Quantitative assessment of mandibular cortical erosion on dental panoramic radiographs for screening osteoporosis,” International Journal of Computer Assisted Radiology and Surgery, vol. 11, no. 11, pp. 2021–2032, 2016. doi: 10.1007/s11548-016-1438-8

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