Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification DOI Creative Commons
Yasin Kaya,

Ezgisu Akat,

Serdar Yıldırım

et al.

Brain and Behavior, Journal Year: 2025, Volume and Issue: 15(5)

Published: May 1, 2025

ABSTRACT Problem Brain tumors are among the most prevalent and lethal diseases. Early diagnosis precise treatment crucial. However, manual classification of brain is a laborious complex task. Aim This study aimed to develop fusion model address certain limitations previous works, such as covering diverse image modalities in various datasets. Method We presented hybrid transfer learning model, Fusion‐Brain‐Net, at automatic tumor classification. The proposed method included four stages: preprocessing data augmentation, deep feature extractions, fine‐tuning, Integrating pre‐trained CNN models, VGG16, ResNet50, MobileNetV2, enhanced comprehensive extraction while mitigating overfitting issues, improving model's performance. Results was rigorously tested verified on public datasets: Br35H, Figshare, Nickparvar, Sartaj. It achieved remarkable accuracy rates 99.66%, 97.56%, 97.08%, 93.74%, respectively. Conclusion numerical results highlight that should be further investigated for potential use computer‐aided diagnoses improve clinical decision‐making.

Language: Английский

Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification DOI Creative Commons
Yasin Kaya,

Ezgisu Akat,

Serdar Yıldırım

et al.

Brain and Behavior, Journal Year: 2025, Volume and Issue: 15(5)

Published: May 1, 2025

ABSTRACT Problem Brain tumors are among the most prevalent and lethal diseases. Early diagnosis precise treatment crucial. However, manual classification of brain is a laborious complex task. Aim This study aimed to develop fusion model address certain limitations previous works, such as covering diverse image modalities in various datasets. Method We presented hybrid transfer learning model, Fusion‐Brain‐Net, at automatic tumor classification. The proposed method included four stages: preprocessing data augmentation, deep feature extractions, fine‐tuning, Integrating pre‐trained CNN models, VGG16, ResNet50, MobileNetV2, enhanced comprehensive extraction while mitigating overfitting issues, improving model's performance. Results was rigorously tested verified on public datasets: Br35H, Figshare, Nickparvar, Sartaj. It achieved remarkable accuracy rates 99.66%, 97.56%, 97.08%, 93.74%, respectively. Conclusion numerical results highlight that should be further investigated for potential use computer‐aided diagnoses improve clinical decision‐making.

Language: Английский

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