Application of Learning Vector Quantization in Digital Image Processing for Skin Disease Detection
DOI:
https://doi.org/10.37859/coscitech.v6i2.9270
Keywords:
LVQ, Skin Disease Detection, Digital Image Processing, Local Binary Pattern Learning Vector Quantization, Deteksi Penyakit Kulit, Pengolahan Citra Digital, Pola Biner Lokal
Abstract
Skin, as the largest human organ, covers more than two square meters and accounts for about 15% of body mass. Consisting of three main layers of epidermis, dermis, and subcutaneous tissue, the skin serves as a physical shield and barrier against infection, injury, and UV radiation. Skin diseases such as chickenpox, monkey pox, measles and herpes are medical challenges that require quick and accurate diagnosis. This study used 520 digital images (130 per category) from Mendeley Data and online sources. The Learning Vector Quantization (LVQ) algorithm was applied for image classification based on the extracted features. Results showed an overall accuracy of 90.74%, with respective accuracies: 97% (chickenpox), 98% (monkey pox), 91% (measles), and 100% (herpes). Evaluation using confusion matrix resulted in accuracy, precision, recall, and F1-score values of 0.91, indicating strong model performance. These findings demonstrate the potential of LVQ as a digital image-based skin disease diagnosis tool.
Downloads
Download data is not yet available.
References
[1] M. M. K. Nielsen, E. Aryal, E. Safari, B. Mojsoska, H. Jenssen, and B. K. Prabhala, “Current state of slc and abc transporters in the skin and their relation to sweat metabolites and skin diseases,” 2021. doi: 10.3390/proteomes9020023.
[2] A. T. Slominski, R. M. Slominski, C. Raman, J. Y. Chen, M. Athar, and C. Elmets, “Neuroendocrine signaling in the skin with a special focus on the epidermal neuropeptides,” 2022. doi: 10.1152/AJPCELL.00147.2022.
[3] I. Peate, “The skin: largest organ of the body,” British Journal of Healthcare Assistants, vol. 15, no. 9, 2021, doi: 10.12968/bjha.2021.15.9.446.
[4] K. Davies and C. Hewitt, “Biological basis of child health 13: structure and functions of the skin, and common children’s skin conditions,” Nurs Child Young People, vol. 34, no. 2, 2022, doi: 10.7748/ncyp.2021.e1359.
[5] D. Datta, B. Madke, and A. Das, “Skin as an endocrine organ: A narrative review,” 2022. doi: 10.25259/IJDVL_533_2021.
[6] P. A. Siregar, N. A. Azwa, A. D. Mrp, and S. Maghfirah, “EPIDEMIOLOGI PENYAKIT MENULAR CACAR AIR,” JK: Jurnal Kesehatan, vol. 1, no. 1, pp. 10–24, 2023.
[7] K. Chadaga et al., “Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review,” Mar. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/diagnostics13050824.
[8] M. Catalina Castro and P. Rojas, “Preventive effectiveness of varicella vaccine in healthy unexposed patients,” Medwave, vol. 20, no. 06, pp. e7982–e7982, Jul. 2020, doi: 10.5867/medwave.2020.06.7982.
[9] Q. Liu et al., “Clinical Characteristics of Human Mpox (Monkeypox) in 2022: A Systematic Review and Meta-Analysis,” Pathogens, vol. 12, no. 1, p. 146, Jan. 2023, doi: 10.3390/pathogens12010146.
[10] G. Sun et al., “Herpes simplex virus type 1 modifies the protein composition of extracellular vesicles to promote neurite outgrowth and neuroinfection,” mBio, vol. 15, no. 2, Feb. 2024, doi: 10.1128/mbio.03308-23.
[11] S. Samsir et al., “Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease,” J Phys Conf Ser, vol. 2394, no. 1, p. 012016, Dec. 2022, doi: 10.1088/1742-6596/2394/1/012016.
[12] R. I. Borman, Y. Fernando, and Y. Egi Pratama Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 339–345, Apr. 2022, doi: 10.29207/resti.v6i2.3954.
[13] D. Bala et al., “MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification,” Neural Networks, vol. 161, pp. 757–775, Apr. 2023, doi: 10.1016/j.neunet.2023.02.022.
[14] R. Archana and P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif Intell Rev, vol. 57, no. 1, 2024, doi: 10.1007/s10462-023-10631-z.
[15] I. Tangkawarow, D. P. Hostiadi, N. S. Fatonah, Mohammad Yazdi, and E. Hariyanti, “Analisis Seleksi Fitur untuk Optimasi Metode Klasifikasi k-NN pada Studi Kasus Penilaian Kinerja Karyawan,” Jurnal Sistem dan Informatika (JSI), vol. 18, no. 1, pp. 18–28, Nov. 2023, doi: 10.30864/jsi.v18i1.593.
[16] R. AŞLIYAN, “Examining Variants of Learning Vector Quantizations According to Normalization and Initialization of Vector Positions,” European Journal of Science and Technology, 2022, doi: 10.31590/ejosat.1222296.
[17] I. N. Abrar, A. Abdullah, and S. Sucipto, “Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 2, pp. 218–228, Jul. 2023, doi: 10.32736/sisfokom.v12i2.1643.
[18] M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.
[19] E. Ismanto, A. Fadlil, and A. Yudhana, “Analisis Perbandingan Model Fully Connected Neural Networks (FCNN) dan TabNet Untuk Klasifikasi Perawatan Pasien Pada Data Tabular,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 5, no. 3, Dec. 2024, [Online]. Available: https://ejurnal.umri.ac.id/index.php/coscitech/article/view/8256
[20] R. Raynold and A. H. Muhammad, “Deep Learning Detection and Classification of Tomato Leaf Disease Using ResNet-50,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 6, no. 1, Apr. 2025, doi: 10.37859/coscitech.v6i1.8501.
[2] A. T. Slominski, R. M. Slominski, C. Raman, J. Y. Chen, M. Athar, and C. Elmets, “Neuroendocrine signaling in the skin with a special focus on the epidermal neuropeptides,” 2022. doi: 10.1152/AJPCELL.00147.2022.
[3] I. Peate, “The skin: largest organ of the body,” British Journal of Healthcare Assistants, vol. 15, no. 9, 2021, doi: 10.12968/bjha.2021.15.9.446.
[4] K. Davies and C. Hewitt, “Biological basis of child health 13: structure and functions of the skin, and common children’s skin conditions,” Nurs Child Young People, vol. 34, no. 2, 2022, doi: 10.7748/ncyp.2021.e1359.
[5] D. Datta, B. Madke, and A. Das, “Skin as an endocrine organ: A narrative review,” 2022. doi: 10.25259/IJDVL_533_2021.
[6] P. A. Siregar, N. A. Azwa, A. D. Mrp, and S. Maghfirah, “EPIDEMIOLOGI PENYAKIT MENULAR CACAR AIR,” JK: Jurnal Kesehatan, vol. 1, no. 1, pp. 10–24, 2023.
[7] K. Chadaga et al., “Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review,” Mar. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/diagnostics13050824.
[8] M. Catalina Castro and P. Rojas, “Preventive effectiveness of varicella vaccine in healthy unexposed patients,” Medwave, vol. 20, no. 06, pp. e7982–e7982, Jul. 2020, doi: 10.5867/medwave.2020.06.7982.
[9] Q. Liu et al., “Clinical Characteristics of Human Mpox (Monkeypox) in 2022: A Systematic Review and Meta-Analysis,” Pathogens, vol. 12, no. 1, p. 146, Jan. 2023, doi: 10.3390/pathogens12010146.
[10] G. Sun et al., “Herpes simplex virus type 1 modifies the protein composition of extracellular vesicles to promote neurite outgrowth and neuroinfection,” mBio, vol. 15, no. 2, Feb. 2024, doi: 10.1128/mbio.03308-23.
[11] S. Samsir et al., “Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease,” J Phys Conf Ser, vol. 2394, no. 1, p. 012016, Dec. 2022, doi: 10.1088/1742-6596/2394/1/012016.
[12] R. I. Borman, Y. Fernando, and Y. Egi Pratama Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 339–345, Apr. 2022, doi: 10.29207/resti.v6i2.3954.
[13] D. Bala et al., “MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification,” Neural Networks, vol. 161, pp. 757–775, Apr. 2023, doi: 10.1016/j.neunet.2023.02.022.
[14] R. Archana and P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif Intell Rev, vol. 57, no. 1, 2024, doi: 10.1007/s10462-023-10631-z.
[15] I. Tangkawarow, D. P. Hostiadi, N. S. Fatonah, Mohammad Yazdi, and E. Hariyanti, “Analisis Seleksi Fitur untuk Optimasi Metode Klasifikasi k-NN pada Studi Kasus Penilaian Kinerja Karyawan,” Jurnal Sistem dan Informatika (JSI), vol. 18, no. 1, pp. 18–28, Nov. 2023, doi: 10.30864/jsi.v18i1.593.
[16] R. AŞLIYAN, “Examining Variants of Learning Vector Quantizations According to Normalization and Initialization of Vector Positions,” European Journal of Science and Technology, 2022, doi: 10.31590/ejosat.1222296.
[17] I. N. Abrar, A. Abdullah, and S. Sucipto, “Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 2, pp. 218–228, Jul. 2023, doi: 10.32736/sisfokom.v12i2.1643.
[18] M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.
[19] E. Ismanto, A. Fadlil, and A. Yudhana, “Analisis Perbandingan Model Fully Connected Neural Networks (FCNN) dan TabNet Untuk Klasifikasi Perawatan Pasien Pada Data Tabular,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 5, no. 3, Dec. 2024, [Online]. Available: https://ejurnal.umri.ac.id/index.php/coscitech/article/view/8256
[20] R. Raynold and A. H. Muhammad, “Deep Learning Detection and Classification of Tomato Leaf Disease Using ResNet-50,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 6, no. 1, Apr. 2025, doi: 10.37859/coscitech.v6i1.8501.
Downloads
Published
2025-08-10
How to Cite
Rizki Akbar Pratama, Barry Ceasar Octariadi, & Syarifah Putri Agustini Alkadri. (2025). Application of Learning Vector Quantization in Digital Image Processing for Skin Disease Detection. Jurnal CoSciTech (Computer Science and Information Technology), 6(2), 148–157. https://doi.org/10.37859/coscitech.v6i2.9270
Issue
Section
Articles
