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Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning. / Shchegoleva, Nadezhda; Pronina, Nadezhda; Zalutskaya, Nataliya; Kiyamov, Jasur.

Computational Science and Its Applications -- ICCSA 2025 Workshops. ed. / Osvaldo Gervasi; Beniamino Murgante; Chiara Garau; Yeliz Karaca; Maria Noelia Faginas Lago; Francesco Scorza; Ana Cristina Braga. Cham : Springer Nature, 2025. p. 264-281 (Lecture Notes in Computer Science; Vol. 15894 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Shchegoleva, N, Pronina, N, Zalutskaya, N & Kiyamov, J 2025, Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning. in O Gervasi, B Murgante, C Garau, Y Karaca, MN Faginas Lago, F Scorza & AC Braga (eds), Computational Science and Its Applications -- ICCSA 2025 Workshops. Lecture Notes in Computer Science, vol. 15894 LNCS, Springer Nature, Cham, pp. 264-281, Computational Science and Its Applications – ICCSA 2025 Workshops, Istanbul, Turkey, 30/06/25. https://doi.org/10.1007/978-3-031-97648-3_18

APA

Shchegoleva, N., Pronina, N., Zalutskaya, N., & Kiyamov, J. (2025). Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning. In O. Gervasi, B. Murgante, C. Garau, Y. Karaca, M. N. Faginas Lago, F. Scorza, & A. C. Braga (Eds.), Computational Science and Its Applications -- ICCSA 2025 Workshops (pp. 264-281). (Lecture Notes in Computer Science; Vol. 15894 LNCS). Springer Nature. https://doi.org/10.1007/978-3-031-97648-3_18

Vancouver

Shchegoleva N, Pronina N, Zalutskaya N, Kiyamov J. Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning. In Gervasi O, Murgante B, Garau C, Karaca Y, Faginas Lago MN, Scorza F, Braga AC, editors, Computational Science and Its Applications -- ICCSA 2025 Workshops. Cham: Springer Nature. 2025. p. 264-281. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-031-97648-3_18

Author

Shchegoleva, Nadezhda ; Pronina, Nadezhda ; Zalutskaya, Nataliya ; Kiyamov, Jasur. / Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning. Computational Science and Its Applications -- ICCSA 2025 Workshops. editor / Osvaldo Gervasi ; Beniamino Murgante ; Chiara Garau ; Yeliz Karaca ; Maria Noelia Faginas Lago ; Francesco Scorza ; Ana Cristina Braga. Cham : Springer Nature, 2025. pp. 264-281 (Lecture Notes in Computer Science).

BibTeX

@inproceedings{4a68db7db4384a79b30dd6079e236aea,
title = "Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning",
abstract = "Brain tumor detection remains one of the most pressing challenges in medical image analysis due to the complexity and variability of tumor structures. This study presents an automated approach for analyzing magnetic resonance imaging (MRI) scans, aimed at identifying pathological cases and localizing abnormal regions. A hybrid classification model based on support vector machines and k-nearest neighbors is used to distinguish between normal and pathological images. For segmentation, a superpixel-based method is applied to highlight tumor areas. The system combines traditional image preprocessing with statistical and textural feature extraction to enhance diagnostic accuracy. Experimental results confirm the effectiveness of the proposed two-stage pipeline in supporting early diagnosis and reducing the cognitive workload of clinicians.",
keywords = "MRI, brain tumor, hybrid classifier",
author = "Nadezhda Shchegoleva and Nadezhda Pronina and Nataliya Zalutskaya and Jasur Kiyamov",
year = "2025",
month = jun,
day = "28",
doi = "10.1007/978-3-031-97648-3_18",
language = "English",
isbn = "978-3-031-97648-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "264--281",
editor = "Osvaldo Gervasi and Beniamino Murgante and Chiara Garau and Yeliz Karaca and {Faginas Lago}, {Maria Noelia} and Francesco Scorza and Braga, {Ana Cristina}",
booktitle = "Computational Science and Its Applications -- ICCSA 2025 Workshops",
address = "Germany",
note = "Computational Science and Its Applications – ICCSA 2025 Workshops ; Conference date: 30-06-2025 Through 03-07-2025",

}

RIS

TY - GEN

T1 - Automated Classification and Segmentation of Brain Tumors on MRI Images Using Superpixel-Based Machine Learning

AU - Shchegoleva, Nadezhda

AU - Pronina, Nadezhda

AU - Zalutskaya, Nataliya

AU - Kiyamov, Jasur

PY - 2025/6/28

Y1 - 2025/6/28

N2 - Brain tumor detection remains one of the most pressing challenges in medical image analysis due to the complexity and variability of tumor structures. This study presents an automated approach for analyzing magnetic resonance imaging (MRI) scans, aimed at identifying pathological cases and localizing abnormal regions. A hybrid classification model based on support vector machines and k-nearest neighbors is used to distinguish between normal and pathological images. For segmentation, a superpixel-based method is applied to highlight tumor areas. The system combines traditional image preprocessing with statistical and textural feature extraction to enhance diagnostic accuracy. Experimental results confirm the effectiveness of the proposed two-stage pipeline in supporting early diagnosis and reducing the cognitive workload of clinicians.

AB - Brain tumor detection remains one of the most pressing challenges in medical image analysis due to the complexity and variability of tumor structures. This study presents an automated approach for analyzing magnetic resonance imaging (MRI) scans, aimed at identifying pathological cases and localizing abnormal regions. A hybrid classification model based on support vector machines and k-nearest neighbors is used to distinguish between normal and pathological images. For segmentation, a superpixel-based method is applied to highlight tumor areas. The system combines traditional image preprocessing with statistical and textural feature extraction to enhance diagnostic accuracy. Experimental results confirm the effectiveness of the proposed two-stage pipeline in supporting early diagnosis and reducing the cognitive workload of clinicians.

KW - MRI

KW - brain tumor

KW - hybrid classifier

UR - https://www.mendeley.com/catalogue/02bb19e7-d941-3948-b24b-c8f48d392c3b/

U2 - 10.1007/978-3-031-97648-3_18

DO - 10.1007/978-3-031-97648-3_18

M3 - Conference contribution

SN - 978-3-031-97648-3

T3 - Lecture Notes in Computer Science

SP - 264

EP - 281

BT - Computational Science and Its Applications -- ICCSA 2025 Workshops

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Garau, Chiara

A2 - Karaca, Yeliz

A2 - Faginas Lago, Maria Noelia

A2 - Scorza, Francesco

A2 - Braga, Ana Cristina

PB - Springer Nature

CY - Cham

T2 - Computational Science and Its Applications – ICCSA 2025 Workshops

Y2 - 30 June 2025 through 3 July 2025

ER -

ID: 139439813