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Optimization of Multi-Section and Partially Augmented Magnetic Resonance Imaging (MRI) Images for Brain Tumor Classification Using ResNet-50

CommIT (Communication and Information Technology) Journal · Vol. 19 No. 1 (2025) · pp. 115–128

DOI
10.21512/commit.v19i1.12467
ISSN
2460-7010, 1979-2484
Published
2025-05-05
Publisher
Bina Nusantara University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Brain tumor diagnosis is challenging due to complex brain anatomy and tumor variability across imaging views. Traditional methods are manual and error-prone, making deep learning, particularly ResNetbased Convolutional Neural Network (CNN), essential for improving accuracy. The research investigates the enhancement of brain tumor classification using Magnetic Resonance Imaging (MRI) images through a novel modification of the ResNet50 model. It specifically addresses data imbalance challenges in medical image analysis. By proposing a targeted approach to partial data augmentation, the researchers aim to overcome limitations in traditional deep-learning classification methodologies, particularly the performance bottlenecks encountered in differentiating complex brain tumor subtypes. The research uses MRI dataset containing 5,249 labeled images (glioma, meningioma, pituitary, no tumor) across axial, coronal, and sagittal planes, highlighting class and view-based imbalances addressed through targeted augmentation. The research employs transfer learning to analyze three scenarios: non-augmented, partially augmented, and rounding-down data. Results reveal that the partially augmented scenario achieves the highest classification accuracy at 85%, significantly surpassing the non-augmented scenario, which peaks at 79%. In contrast, the rounding-down scenario yields only 60.16% accuracy during validation, highlighting the negative impact of drastically reducing data quantities. The unique contribution lies in demonstrating how strategic partial augmentation can enhance pattern recognition and mitigate overfitting risks, particularly in medical imaging where precise differentiation is crucial. The findings highlight the critical role of nuanced data distribution in enhancing model robustness, as evidenced by improved pattern recognition and reduced overfitting risks in the augmented scenario.

Keywords

Collaboration network of the authors

33 authors · 94 co-authorship links · 30 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Handayani Makassar
  • Universiti Malaysia Terengganu
  • University of Pembangunan Nasional Veteran Jawa Timur
  • Universitas Nahdlatul Ulama Surabaya
  • Hasanuddin University
  • Universitas Wijaya Kusuma Surabaya
  • Universitas Airlangga
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Deshinta Arrova DewiAffiliation unknown3
Sayyidah HumairahAffiliation unknown3
Mohd Afifi JusohUniversiti Malaysia Terengganu2
Muhamad Zalani DaudUniversiti Malaysia Terengganu2
Teguh HerlambangUniversitas Nahdlatul Ulama Surabaya2
Pangestu Sandya Etniko SiagianAffiliation unknown2
Wan Suryani Wan AwangAffiliation unknown2
Abdul Latief ArdaUniversitas Handayani Makassar1
Muh. Fauzan SaidUniversitas Handayani Makassar1
Syamsu AlamHasanuddin University1
Shofiya SyidadaUniversitas Wijaya Kusuma Surabaya1
Noor Fitria AzzahraAffiliation unknown1
Budi Sulistiyo NugrohoUniversity of Pembangunan Nasional Veteran Jawa Timur1
Yasmin UlayyaAffiliation unknown1
Mohammad IdhomAffiliation unknown1
Nyoman JuniartaUniversité de Lorraine1
Aji Akbar FirdausUniversitas Airlangga1
Ragil Puji RahayuUniversitas Nahdlatul Ulama Surabaya1
Dina NovitaUniversitas Muhammadiyah Surabaya1
Arasy AlimudinUniversitas Narotama1
Hamzah ArofUniversity of Malaya1
Zulkifli Mohd YusopAffiliation unknown1
Aliashim AlbaniAffiliation unknown1
Wahyu CaesarendraUniversiti Brunei Darussalam1
I Gede Susrama Mas DiyasaAffiliation unknown1
Fara Disa DurryAffiliation unknown1
Wahyu Dwi LestariAffiliation unknown1
Rangga Laksana AryanandaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.binus.ac.id/index.php/commit/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Optimization of Multi-Section and Partially Augmented Magnetic Resonance Imaging (MRI) Images for Brain Tumor Classification Using ResNet-50</dc:title>
	<dc:creator>Diyasa, I Gede Susrama Mas</dc:creator>
	<dc:creator>Sunarko, Victor Immanuel</dc:creator>
	<dc:creator>Puspaningrum, Eva Yulia</dc:creator>
	<dc:creator>Asy'ari, Vaizal</dc:creator>
	<dc:creator>Ibrahim, Mohd Zamri</dc:creator>
	<dc:subject xml:lang="en-US">Magnetic Resonance Imaging (MRI)</dc:subject>
	<dc:subject xml:lang="en-US">Brain Tumor</dc:subject>
	<dc:subject xml:lang="en-US">ResNet-50</dc:subject>
	<dc:description xml:lang="en-US">Brain tumor diagnosis is challenging due to complex brain anatomy and tumor variability across imaging views. Traditional methods are manual and error-prone, making deep learning, particularly ResNetbased Convolutional Neural Network (CNN), essential for improving accuracy. The research investigates the enhancement of brain tumor classification using Magnetic Resonance Imaging (MRI) images through a novel modification of the ResNet50 model. It specifically addresses data imbalance challenges in medical image analysis. By proposing a targeted approach to partial data augmentation, the researchers aim to overcome limitations in traditional deep-learning classification methodologies, particularly the performance bottlenecks encountered in differentiating complex brain tumor subtypes. The research uses MRI dataset containing 5,249 labeled images (glioma, meningioma, pituitary, no tumor) across axial, coronal, and sagittal planes, highlighting class and view-based imbalances addressed through targeted augmentation. The research employs transfer learning to analyze three scenarios: non-augmented, partially augmented, and rounding-down data. Results reveal that the partially augmented scenario achieves the highest classification accuracy at 85%, significantly surpassing the non-augmented scenario, which peaks at 79%. In contrast, the rounding-down scenario yields only 60.16% accuracy during validation, highlighting the negative impact of drastically reducing data quantities. The unique contribution lies in demonstrating how strategic partial augmentation can enhance pattern recognition and mitigate overfitting risks, particularly in medical imaging where precise differentiation is crucial. The findings highlight the critical role of nuanced data distribution in enhancing model robustness, as evidenced by improved pattern recognition and reduced overfitting risks in the augmented scenario.</dc:description>
	<dc:publisher xml:lang="en-US">Bina Nusantara University</dc:publisher>
	<dc:date>2025-05-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.binus.ac.id/index.php/commit/article/view/12467</dc:identifier>
	<dc:identifier>10.21512/commit.v19i1.12467</dc:identifier>
	<dc:source xml:lang="en-US">CommIT (Communication and Information Technology) Journal; Vol. 19 No. 1 (2025): CommIT Journal ; 115-128</dc:source>
	<dc:source>2460-7010</dc:source>
	<dc:source>1979-2484</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.binus.ac.id/index.php/commit/article/view/12467/5408</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 I Gede Susrama Mas Diyasa, Victor Immanuel Sunarko, Eva Yulia Puspaningrum, Vaizal Asy'ari, Mohd Zamri Ibrahim</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>