Klasifikasi Tumor Payudara Pada Citra Ultrasonografi Menggunakan Multi-fitur Tekstur dan Support Vector Machine

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Published: Apr 16, 2024
DOI: https://doi.org/10.32672/jse.v9i2.1481
Keywords:
breast tumor, features extraction, texture, classification

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Rahmad Hidayat
Jurusan Teknologi Informasi dan Komputer Politeknik Negeri Lhokseumawe
Huzaeni
Jurusan Teknologi Informasi dan Komputer Politeknik Negeri Lhokseumawe
Mahdi
Jurusan Teknologi Informasi dan Komputer Politeknik Negeri Lhokseumawe
M. Khadafi
Jurusan Teknologi Informasi dan Komputer Politeknik Negeri Lhokseumawe
Muhammad Davi
Jurusan Teknologi Informasi dan Komputer Politeknik Negeri Lhokseumawe

Abstract

Breast cancer is a prevalent type of cancer affecting women worldwide. Additionally, Globocan reported nearly 400,000 new cancer cases in Indonesia in 2020, with 16% being breast cancer. The Ministry of Health has prioritised breast cancer treatment due to the high number of cases. Early detection is a crucial factor in increasing patient life expectancy. Stage 1 breast cancer, for instance, has a 5-year life expectancy of 100%. Breast ultrasound or mammary ultrasound is a commonly used method to detect various breast problems, including cysts and tumors. It is a relatively easy procedure, and the necessary equipment is generally available at Health Facility 1. Texture features are extracted from breast ultrasound images using Gabor and Gray Level Co-occurrence Matrix (GLCM) techniques. The resulting feature vector is then selected and its dimensions reduced to simplify the computing process. This vector is then used to train an SVM classifier to distinguish between benign and malignant cases. The accuracy of the classifier is 0.67 (training) and 0.66 (validation). Meanwhile, the loss obtained during training was 0.77 and during validation was 0.84. Further improvement is required for the accuracy of the model to be applicable.

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How to Cite
[1]
Rahmad Hidayat, Huzaeni, Mahdi, M. Khadafi, and Muhammad Davi, “Klasifikasi Tumor Payudara Pada Citra Ultrasonografi Menggunakan Multi-fitur Tekstur dan Support Vector Machine”, JSE, vol. 9, no. 2, Apr. 2024.
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Vol. 9 No. 2 (2024): Edisi Khusus April
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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

N. Micallef, D. Seychell, and C. J. Bajada, “Exploring the U-Net ++ Model for Automatic Brain Tumor Segmentation,” IEEE Access, vol. 9, pp. 125523–125539, 2021, doi: 10.1109/ACCESS.2021.3111131.

M. Bella, A. Rasyid, F. Arnia, and K. Munadi, “Histogram Statistics and GLCM Features of Breast Thermograms for Early Cancer Detection,” pp. 120–124, 2018.

A. A. Khan and A. S. Arora, “Breast Cancer Detection Through Gabor Filter Based Texture Features Using Thermograms Images,” pp. 412–417, 2018.

T. Nguyen, T. Nguyen, and B. Ngo, “A GLCM Algorithm for Optimal Features of Mammographic Images for Detection of Breast Cancer,” pp. 295–299, 2021.

M. Thohir, D. Candra, and R. Novitasari, “Classification of Colposcopy Data Using GLCM- SVM on Cervical Cancer,” pp. 373–378, 2020.

T. T. Htay and S. S. Maung, “Early Stage Breast Cancer Detection System using GLCM feature extraction and K-Nearest Neighbor ( k-NN ) on Mammography image,” no. Iscit, pp. 171–175, 2018.

T. Imtiaz and S. Rifat, “Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images,” 2020.

A. S. Elkorany, M. Marey, K. M. Almustafa, and Z. F. Elsharkawy, “Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms,” IEEE Access, vol. 10, no. July, pp. 69688–69699, 2022, doi: 10.1109/ACCESS.2022.3186021.

J. Jiang et al., “Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images,” vol. 38, no. 1, pp. 134–144, 2019.

C. Zhang and D. Wu, “A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmentation,” 2019.

B. Wang, X. Lv, M. I. N. Li, and J. Wang, “Classification of Cervical Biopsy Images Based on LASSO and EL-SVM,” vol. 8, 2020.

T. Heseltine, N. Pears, and J. Austin, “Evaluation of image preprocessing techniques for eigenface-based face recognition,” Second Int. Conf. Image Graph., vol. 4875, no. 2002, p. 677, 2002, doi: 10.1117/12.477052.

D. Zhang, A. Wong, M. Indrawan, and G. Lu, “Content-based Image Retrieval Using Gabor Texture Features,” IEEE Trans. PAMI, vol. 3656 LNCS, pp. 13–15, 2000.

A. N. Rumaksari, S. Sumpeno, and A. D. Wibawa, “Background subtraction using spatial mixture of Gaussian model with dynamic shadow filtering,” 2017 Int. Semin. Intell. Technol. Its Appl. Strength. Link Between Univ. Res. Ind. to Support ASEAN Energy Sect. ISITIA 2017 - Proceeding, vol. 2017-Janua, pp. 296–301, 2017, doi: 10.1109/ISITIA.2017.8124098.

S. Alameen and M. E. M. Gar-Elnabi, “Study of Glcm for Diagnosis of Liver Diseases From Abdominal Ct Images,” no. August, 2016.

H. Abdi and L. J. Williams, “Principal component analysis,” Wiley Interdiscip. Rev. Comput. Stat., vol. 2, no. 4, pp. 433–459, 2010, doi: 10.1002/wics.101.

X. Y. Wang, L. L. Liang, W. Y. Li, D. M. Li, and H. Y. Yang, “A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function,” J. Vis. Commun. Image Represent., vol. 38, pp. 256–275, 2016, doi: 10.1016/j.jvcir.2016.03.008.

H. Yuan, “An SVM-Based AdaBoost Cascade Classifier for Sonar Image,” vol. 8, pp. 115857–115864, 2020, doi: 10.1109/ACCESS.2020.3004473.

R. Liu, Y. Wang, T. Baba, D. Masumoto, and S. Nagata, “SVM-based active feedback in image retrieval using clustering and unlabeled data,” Pattern Recognit., vol. 41, no. 8, pp. 2645–2655, 2008, doi: 10.1016/j.patcog.2008.01.023.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A Training Algorithm for Optimal Margin Classiiers,” Proc. fifth Annu. Work. Comput. Learn. theory, vol. 5, no. 3, pp. 144–152, 1992, doi: 10.1.1.21.3818.