slot gacor slot gacor hari ini slot gacor 2025 demo slot pg slot gacor slot gacor
Support Vector Machine, Naive Bayes, and Artificial Neural Network Back Propagation Comparison in Detecting Brain Tumor | Triadyaksa | Jurnal Kedokteran Diponegoro (Diponegoro Medical Journal)

DOI: https://doi.org/10.14710/dmj.v13i4.45462

Support Vector Machine, Naive Bayes, and Artificial Neural Network Back Propagation Comparison in Detecting Brain Tumor

*Pandji Triadyaksa orcid scopus  -  Physics Department, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Harisma Zaini Ahmad  -  Physics Department, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Indras Marhaendrajaya  -  Physics Department, Faculty of Science and Mathematics, Diponegoro University, Indonesia

Citation Format:
Abstract

Brain tumors are abnormal tissue that grow uncontrolled and affect a patient's neurological function. Brain tumors come in different shapes and characteristics. Moreover, its location also differs for each patient. Brain tumors can be detected using machine learning algorithms using magnetic resonance imaging (MRI) images. However, a different machine-learning comparison is limited and needs further investigation. This study aims to compare three machine-learning methods, i.e., Support Vector Machine (SVM), Naive Bayes (NB), and Artificial Neural Network Back Propagation (ANN-BP) algorithms for detecting brain tumors. Before the comparison started, MRI image quality was enhanced by performing denoising, histogram equalization, and thresholding. After that, Gray Level Co-occurrence Matrix feature extraction was performed. MRI brain images in JPEG format were acquired from an open-access database. One thousand brain tumor and 1000 normal tumor images are used as the training data, while 100 brain tumor and 100 normal tumor images are used as testing data. Each algorithm's accuracy, precision, sensitivity, and Matthews Correlation Coefficient (MCC) are evaluated and reported. The study showed that the SVM algorithm acquired the highest performance in detecting brain tumors, followed by ANN-BP and NB. The highest accuracy, precision, sensitivity, and MCC values for testing in SVM were 98,75%, 98,22%, 99,30%, and 0,9751, respectively. Meanwhile, in testing, the highest accuracy, precision, sensitivity, and MCC values were 90.50%, 98.80%, 82.00%, and 0.8220, respectively. In conclusion, this study showed the superiority of the SVM algorithm in detecting brain tumor compared to ANN-BP and NB by performing image enhancement steps and GLCM feature extraction before its detection.

Fulltext View|Download
Keywords: Artificial Neural Network, Gray Level Co-occurrence Matrix, Support Vector Machine, Naive Bayes.

Article Metrics:

Article Info
Section: Original Articles
Language : EN
Recent articles
The Relationship Between Social Media Addiction and Self-Esteem in Medical Students of Diponegoro University The Effect of Moringa Leaves Extracts on MDA Level in Male Mice Exposed to Electromagnetic Radiation of Mobile Phones COMPARISON of BMI and HbA1c 3 MONTHS POST ROUX-EN-Y GASTRIC BYPASS and SLEEVE GASTRECTOMY at SUMBER WARAS HOSPITAL JAKARTA More recent articles
  1. Abdusalomov, A.B., Mukhiddinov, M., Whangbo, & T.K. Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging. Cancers, 2023; 15, 4172
  2. Martucci, M., Russo, R., Schimperna, F., D’Apolito, G., Panfili, M., Grimaldi, A., Perna, A., Ferranti, A.M., Varcasia, G., Giordano, C., & Gaudino, S. Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives. Biomedicines, 2023; 11, 364
  3. Jaju, A., Li, Y., Dahmoush, H., Gottardo, N.G., Laughlin, S., Mirsky, D., Panigrahy, A., Sabin, N.D., Shaw, D., Storm, P.B., Poussaint, T.Y., Patay, Z., & Bhatia, A. Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPROncology Committee/ASPNR White Paper. Pediatr Blood Cancer, 2023; 70(Suppl. 4), e30147
  4. Solanki, S., Singh, U. P., Chouhan, S. S., & Jain, S. Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview. IEEE Access, 2023; 11, 12870-12886
  5. Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A.M., & Hamed, H.F.A. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned, Magnetic Resonance Imaging, 2019; 61, 300-318
  6. Stadlbauer, A., Marhold, F., Oberndorfer, S., Heinz, G., Buchfelder, M., Kinfe, T.M., & Meyer-Bäse, A. Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data. Cancers, 2022; 14, 2363
  7. Payabvash, S., Aboian, M., Tihan, T., & Cha, S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Front. Oncol. 2020; 10,71
  8. El-Dahshan, E.S.A., Hosny, T., & Salem, A.B.M. Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing, 2010; 20 (2), 433-441
  9. Gurunathan, A, & Krishnan, B.A. Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor. Brain Imaging Behav., 2022; 16(3), 1410-1427
  10. Dheepak, G., J, A.C., & Vaishali, D. Brain tumor classification: a novel approach integrating GLCM, LBP and composite features. Front Oncol., 2024; 13, 1248452
  11. Liu, H., Jin, X., Liu, L., & Jin, X. Low-Dose CT Image Denoising Based on Improved DD-Net and Local Filtered Mechanism. Comput Intell Neurosci., 2022; 2692301
  12. Dyke, R.M., & Hormann, K. Histogram equalization using a selective filter. Vis Comput., 2023; 39(12), 6221-6235
  13. Sharma, S.R., Alshathri, S., Singh, B., Kaur, M., Mostafa, R.R., El-Shafai, W. Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI. Diagnostics (Basel)., 2023; 13(5), 925
  14. Chicco, D., & Jurman, G. The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Min., 2023; 16(1), 4
  15. Shah, Y.S., Hernandez-Garcia, L., Jahanian, H., & Peltier, S.J. Support vector machine classification of arterial volume-weighted arterial spin tagging images. Brain Behav., 2016; 6(12), e00549
  16. Yang, G., Zhang, Y., Yang, J. Ji, G., Dong, Z., Wang, S., Feng, C., & Wang, Q. Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl., 2016; 75, 15601–15617
  17. Larroza, A., Moratal, D., Paredes-Sánchez, A., Soria-Olivas, E., Chust, M.L., Arribas, L.A., Arana, E. Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. J Magn Reson Imaging, 2015; 42(5):1362-8
  18. Hu, X., Wong, K.K., Young, G.S., Guo, L., & Wong, S.T. Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging, 2011; 33(2):296-305
  19. Lozano-Vázquez, L.V., Miura, J., Rosales-Silva, A.J., Luviano-Juárez, A., & Mújica-Vargas, D. Analysis of Different Image Enhancement and Feature Extraction Methods. Mathematics, 2022; 10(14):2407

Last update:

No citation recorded.

Last update:

No citation recorded.