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All Journal Asian Australasian Neuro and Health Science Journal (AANHS-J) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Keswani, Ryan Rhiveldi
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience

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Classification of brain tumor based on shape and texture features and machine learning Rizki, M. Alfi; Faisal, Mohammad Reza; Farmadi, Andi; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Bachtiar, Adam Mukharil; Keswani, Ryan Rhiveldi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/27236g49

Abstract

Information from brain tumour visualisation using MRI can be used for brain tumour classification. The information can be extracted using different feature extraction techniques. This study compares shape-based feature extraction such as Zernike Moment (ZM), and Pyramid Histogram of Oriented Gradients (PHOG) with texture-based feature extraction such as Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG) in brain tumour classification. This research aims to find out which feature extraction is better for handling brain tumour images through the accuracy and f1-score produced. This research proposes to combine each feature based on its approach, i.e. ZM+PHOG for shape-based feature extraction and LBP+GLCM+HOG for texture-based feature extraction with default parameters from the library and modified parameters configured based on previous research. The dataset used comes from Kaggle and has three classes: meningioma, glioma, and pituitary. The machine learning classification models used are Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbours (KNN) with default parameters from the library. The models were evaluated using 10-fold stratified cross-validation. This research resulted in an accuracy and f1-score of 84% for texture-based feature extraction with modified parameters in RF classification. In comparison, shape-based feature extraction resulted in accuracy and f1-score of 70% and 68% with modified parameters in RF classification. From the results, it can be concluded that texture-based feature extraction is better in handling brain tumour images compared to shape-based feature extraction. This study suggests that focusing on texture details in feature extraction can significantly improve classification performance in medical imaging such as brain tumours
Predictor of Percutaneous Radio-Frequency Rhizotomy Outcomes for Trigeminal Neuralgia: A Single Center Prospective Cohort Study Prasetya, Mustaqim; Wardhana, Aji Wahyu; Adidharma, Peter; Yefri, Rezka Fadillah; Sulistyanto, Adi; Fadhil, Fadhil; Oswari, Selfy; Keswani, Ryan Rhiveldi; Kusdiansah, Muhammad; Aji, Yunus K.; Arham, Abrar
Asian Australasian Neuro and Health Science Journal (AANHS-J) Vol. 8 No. 01 (2026): AANHS Journal
Publisher : Talenta Universitas Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/aanhs-j.v8i01.25357

Abstract

Background: Percutaneous radiofrequency rhizotomy (PRFR) offers a minimally invasive alternative for trigeminal neuralgia (TN) patients who are ineligible for microvascular decompression (MVD) or who suffer from refractory TN following MVD. However, clinical outcome predictors for PRFR, particularly in low-to-middle-income countries, remain insufficiently documented. Objectives: This study aims to (1) present the clinical characteristics of patients undergoing PRFR at a national tertiary brain center, and (2) identify clinical variables that predict optimal surgical outcomes. Methods: This prospective cohort study included 37 surgery-naïve and post-MVD recurrent TN patients who underwent PRFR between 2014 and 2020. Patient characteristics and offending pathologies were documented. Postoperative outcomes were assessed using the Barrow Neurological Institute (BNI) scales and the Numerical Rating Scale (NRS). Univariate and bivariate analyses were utilized to construct prediction models. Results: The cohort had a mean age of 59 ± 15 years. Among the patients, 51.4% were surgery-naïve, while 48.6% had a history of previous MVD. The PRFR procedure yielded significant NRS improvements in both the surgery-naïve (p < 0.001) and post-MVD (p = 0.001) groups, with no statistically significant difference in pain reduction between the two (p = 0.151). Preoperative identification of the offending pathology was a significant predictor of surgical success (p = 0.019), with small artery compression showing the highest rate of satisfactory outcomes. Conclusion: PRFR provides profound and immediate pain relief for both surgery-naïve patients and those with post-MVD recurrences. The nature of the offending pathology serves as a crucial clinical predictor for achieving optimal outcomes, making PRFR a highly reliable and cost-effective therapeutic pillar in the management of refractory TN.
Co-Authors Adidharma, Peter Aji, Yunus K. Andi Farmadi Arham, Abrar Bachtiar, Adam Mukharil Dodon Turianto Nugrahadi Fadhil Fadhil, Fadhil Kusdiansah, Muhammad Muhammad Reza Faisal, Muhammad Reza Oswari, Selfy Prasetya, Mustaqim Rizki, M. Alfi Saragih, Triando Hamonangan Sulistyanto, Adi Wardhana, Aji Wahyu Yefri, Rezka Fadillah