Effect of Using GLCM and LBP+HOG Feature Extraction on SVM Method in Classification of Human Skin Disease Type

Friska  Eka Khoirunisa; Novrido  Charibaldi

doi:10.25139/inform.v9i2.8275

Friska Eka Khoirunisa Informatics Department, Universitas Pembangunan Nasional Veteran Yogyakarta
Novrido Charibaldi Informatics Department, Universitas Pembangunan Nasional Veteran Yogyakarta

https://doi.org/10.25139/inform.v9i2.8275

Abstract views: 270 ,

PDF downloads: 202

Keywords: Human Skin Disease, LBP, HOG, GLCM, SVM

Abstract

Skin diseases have been ranked third out of ten diseases suffered by outpatients in many hospitals in Indonesia. The public often underestimates these diseases because they are considered not to cause death. In general, dermatologists diagnose skin diseases using the biopsy process, but the biopsy process is quite expensive and can cause injury to the skin. Each skin disease has different texture and shape characteristics, so classification can be used to distinguish the type of skin disease. This study compares LBP+HOG and GLCM feature extraction with the SVM classification method to determine the best feature extraction in skin disease classification. This research compares GLCM and LBP+HOG feature extraction using the SVM method. GLCM is used to measure the relationship between pixel intensities in the image, while LBP+HOG combines information about the texture and shape of the image. The test results show that the GLCM feature extraction method with SVM classification achieves an accuracy of 74%. In this test, the parameters C=100 and the features used are homogeneity, contrast, energy, correlation, ASM, and dissimilarity. Meanwhile, extraction with LBP+HOG resulted in an accuracy of 68%.

References

R. Adawiyah and D. I. Mulyana, "Optimasi Deteksi Penyakit Kulit Menggunakan Metode Support Vector Machine (SVM) dan Gray Level Co-occurrence Matrix (GLCM)," INFORMASI (Jurnal Informatika dan Sistem Informasi), vol. 14, no. 1, 2022.

R. Yohannes and M. E. Al Rivan, “Klasifikasi Jenis Kanker Kulit Menggunakan CNN-SVM,” Jurnal Algoritme, vol. 2, pp. 133–144, 2022.

Y. Fauzia Achmad, A. Yulfitri, and M. B. Ulum, “Identifikasi Jenis Jerawat Berdasarkan Tekstur Menggunakan GLCM dan Backpropagation,” Jurnal Sains Manajemen Informatika dan Komputer, vol. 20, no. 2, 2021.

W. I. Praseptiyana, A. W. Widodo, and M. A. Rahman, "Pemanfaatan Ciri Gray Level Co-occurrence Matrix (GLCM) Untuk Deteksi Melasma Pada Citra Wajah," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 11, 2019.

P. R. Hegde, M. M. Shenoy, and B. H. Shekar, "Comparison of Machine Learning Algorithms for Skin Disease Classification Using Color and Texture Features," IEEE Xplore, 2018.

M. Ezar Al Rivan and Orlando, “Klasifikasi Jenis Kanker Kulit Manusia Menggunakan Convolutional Neural Network,” MDP STUDENT CONFERENCE (MSC), vol. 2, 2023.

G. Neela, K. Babu, and V. J. Peter, "Skin Cancer Detection Using Support Vector Machine with Histogram of Oriented Gradients Features," Ictact Journal on Soft Computing, vol. 11, no. 02, 2021.

M. A. Rahayu, I. R. Magdalena, and R. Rahmania, “Identifikasi Penyakit Kulit Menggunakan Histogram of Oriented Gradients Dan Jaringan Syaraf Tiruan Backpropagation,” e-Proceeding of Engineering, vol. 6, no. 2, 2019.

H. S. Kusumastuti Aji Putri, C. Atika Sari, D. R. I. M. Setiadi, and E. H. Rachmawanto, "Classification of Skin diseases types using naïve bayes classifier based on local binary pattern features" 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 2020.

N. Akmalia, P. Sihombing, and Suherman, "Skin Diseases Classification Using Local Binary Pattern and Convolutional Neural Network," Telecommunication and Computer Engineering (ELTICOM), 2019.

R. Pujiyanti Hidayat, I. Bambang Hidayat, drg Yuti Malinda, and M. Kes, "Identificaton of Lip Print Pattern on Human Identity Using Histogram of Oriented Gradients And Support Vector Machine For Biometric Forensic Application," e-Proceeding of Engineering, vol. 5, no. 3, 2018.

D. D. Nur Cahyo et al., “Sentiment Analysis for IMDb Movie Review Using Support Vector Machine (SVM) Method,” Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 8, no. 2, 2023.

T. F. iraz Bintang Nusantara and R. Dwi Atmaja, “Klasifikasi Jenis Kulit Wajah Pria Berdasarkan Tekstur Menggunakan Metode Gray Level Co-Occurrence Matrix (GLCM) dan Support Vector Machine (SVM),”, vol. 5, no. 2, 2018.

N. L. Chusna, M. I. Shalahudin, U. Riyanto, and A. D. Alexander, “Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, 2022.

L. M. Wisudawati, “Klasifikasi Tumor Jinak Dan Tumor Ganas Pada Citra Mammogram Menggunakan Gray Level Co-Occurrence Matrix (Glcm) Dan Support Vector Machine (SVM),” Jurnal Ilmiah Informatika Komputer, vol. 26, no. 2, 2021.

D. P. N. Lyrawati, “Deteksi Ujaran Kebencian Pada Twitter Menjelang Pilpres 2019 Dengan Machine Learning,” Jurnal Ilmiah Matematika, vol. 7, no. 3, 2019.

R. Agustina, R. Magdalena, and N. K. C. Pratiwi, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, no. 2, 2022.