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Scopus YÖKSİS DOI Eşleşti SJR Q1

Brain Tumor Detection with Transfer Learning Models Based on Attention Modules

Arabian Journal for Science and Engineering · Ocak 2026

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YÖKSİS Kayıtları
Brain Tumor Detection with Transfer Learning Models Based on Attention Modules
Arabian Journal for Science and Engineering · 2026 SCI-Expanded
Dr. Öğr. Üyesi ZÜLEYHA YILMAZ ACAR →
YÖKSİS ISSN Eşleşmesi

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Makale Bilgileri

Dergi Arabian Journal for Science and Engineering
ISSN2193567X
Yayın TarihiOcak 2026
DOI10.1007/s13369-026-11118-z
Scopus ID2-s2.0-105030132557
Özet The classification of brain tumors through Magnetic Resonance Imaging (MRI) is of paramount importance for the facilitation of early diagnosis and the formulation of treatment strategies; however, manual interpretation continues to be labor-intensive and susceptible to subjective bias. This manuscript proposes a hybrid deep learning framework that integrates transfer learning with attention mechanisms and traditional machine learning methodologies to enhance the detection of brain tumors. In particular, the Convolutional Block Attention Module (CBAM) is integrated into pretrained Convolutional Neural Networks—including ResNet50, DenseNet121, MobileNetV2, InceptionV3, and InceptionResNetV2—by positioning the attention module in advance of the global average pooling layer. This integration serves to augment the spatial and channel-wise emphasis on features pertinent to tumors. Subsequently, deep features gleaned from the attention-enhanced networks are subjected to classification employing a Support Vector Machine (SVM), chosen for its resilience when applied to small and imbalanced datasets. The proposed architecture is evaluated using three public brain MRI datasets. Research findings demonstrate that incorporating CBAM leads to a notable enhancement in classification accuracy across various architectural models, with DenseNet121 delivering the most consistent performance. Furthermore, the CBAM + SVM combination outperforms conventional Softmax classifiers, demonstrating enhanced generalizability and diagnostic reliability. This work highlights the efficacy of attention-guided transfer learning models in medical imaging and supports their integration into practical clinical decision-making systems.

Yazarlar (2)

1
Mehr Ali Qasimi
ORCID: 0009-0005-4648-0024
2
Züleyha Yılmaz Acar

Anahtar Kelimeler

Attention mechanism Brain tumor classification CBAM MRI Support vector machine Transfer learning

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Arabian Journal for Science and Engineering
Q1
SJR Skoru0,545
H-Index89
ÜlkeGermany
Multidisciplinary (Q1)
Dergi sayfasına git

Metrikler

1
Atıf
2
Yazar
6
Anahtar Kelime

Sistemimizdeki Yazarlar

ZY
ZÜLEYHA YILMAZ ACAR
Dr. Öğr. Üyesi

Hızlı Erişim

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