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Sumarti, Heni
Jurusan Fisika, Fakultas Sains Dan Teknologi, Universitas Islam Negeri Walisongo Semarang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Physics Other

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Journal : Journal of Physics and Its Applications

 1 
Classification of CT Scan Images of Stroke Patients and Normal Brain Based on Histogram, GLCM, and GLRLM Texture Features using K-Nearest Neighbor Azizah, Fitria Kholbi; Putri, Diana Salsabila; Permana, Riyan; Sumarti, Heni; Darma, Panji Nursetia
Journal of Physics and Its Applications Vol 7, No 4 (2025): November 2025
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v7i4.27259

Abstract

Stroke is a major neurological disorder requiring rapid and accurate diagnosis for effective treatment. Computerized Tomography (CT) scanning provides detailed brain imaging but requires expert interpretation. This study aims to develop an automated classification system to distinguish between normal and stroke-affected brain CT scan images using texture feature analysis, providing enhanced accuracy and robustness compared to existing single-feature approaches. A total of 200 CT scan images (100 normal, 100 stroke cases) from the Kaggle database were analyzed. Texture features were extracted using Histogram, Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM) analysis. The KNN algorithm was evaluated using percentage split validation, with the training set ranging from 50% to 70% of the data. The KNN classifier achieved optimal performance with 93% accuracy, 91% precision, and 96% recall using a 50% training set, demonstrating its potential as a diagnostic support tool for healthcare professionals to facilitate faster diagnosis and treatment decisions. The integration of multiple texture analysis methods showed superior performance compared to individual feature extraction techniques. Histogram features contributed significantly to classification accuracy by enhancing the detection of tissue heterogeneity. Texture analysis revealed significant differences between normal and stroke images in entropy, contrast, and correlation parameters. The proposed method successfully classifies CT scan images of normal and stroke-affected brains with high accuracy, demonstrating potential for clinical implementation in automated stroke screening and diagnostic support.
Co-Authors Adrial, Rico Affa Ardhi Saputri Affa Ardhi Saputri Agus Sudarmanto Alvania Nabila Tasyakuranti Alvania Nabila Tasyakuranti Alvania Nabila Tasyakuranti Alvnia Nabila Tasyakuranti Anggita, Sheilla Rully Arifah Riana AYU WULANDARI Azizah, Fitria Kholbi Azzahra, Jannatul Firdausa BELLA JULIA Darma, Panji Nursetia Edi Daenuri Anwar Edison, Rizki Edmi Fachrizal Rian Pratama Fahira Septiani Fahira Septiani Farah Alfiana Na’ila Fariyani, Qisthi Fina Mushoffa Firman Hardianto Gideon, Samuel Hadi Kusuma, Hamdan Hamdan Hadi Kusuma Hamdan Hadi Kusuma HAMDAN HADI KUSUMA Hamdan Hadi Kusuma Hani Nur Endah Hartono Hartono Huwaidah, Indah Rifdah Irman Said Prastyo Irman Said Prastyo Isnawati, Nurul Embun Istikomah Istikomah Istikomah Istikomah Istikomah Istikomah Kholidah Kholidah, Kholidah Laelatul Munawaroh Lailiyatu Latifah Latifatul Istianah Maesyaroh, Uhty Maya Shofani Melany Puspa Damayanti Muhammad Ghozali Muhammad Labib Muhammad Syafiul Huda Nuryani, Siska Permana, Riyan Putri Diah Pitaloka Putri, Diana Salsabila Qolby Sabrina Qolby Sabrina, Qolby Rahmani, Tara Puri Ducha Rahmawati, Aida Samuel Gideon Sari, Ice Uliya Septiani, Fahira Sheilla Rully Anggita Siska Nuryani Susilawati Susilawati Syntia Anggraeni Tasyakuranti, Alvania Nabila Tika Rahmawati Tria Nurmar’atin Triana, Devi Uhty Maesyaroh Wahyu Caesarendra