Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification
ABSTRACT Problem Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task. Aim This study aimed to develop a fusion model to address certain limitations of prev...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Brain and Behavior |
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| Online Access: | https://doi.org/10.1002/brb3.70520 |
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| _version_ | 1850141789440180224 |
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| author | Yasin Kaya Ezgisu Akat Serdar Yıldırım |
| author_facet | Yasin Kaya Ezgisu Akat Serdar Yıldırım |
| author_sort | Yasin Kaya |
| collection | DOAJ |
| description | ABSTRACT Problem Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task. Aim This study aimed to develop a fusion model to address certain limitations of previous works, such as covering diverse image modalities in various datasets. Method We presented a hybrid transfer learning model, Fusion‐Brain‐Net, aimed at automatic brain tumor classification. The proposed method included four stages: preprocessing and data augmentation, fusion of deep feature extractions, fine‐tuning, and classification. Integrating the pre‐trained CNN models, VGG16, ResNet50, and MobileNetV2, the model enhanced comprehensive feature extraction while mitigating overfitting issues, improving the model's performance. Results The proposed model was rigorously tested and verified on four public datasets: Br35H, Figshare, Nickparvar, and Sartaj. It achieved remarkable accuracy rates of 99.66%, 97.56%, 97.08%, and 93.74%, respectively. Conclusion The numerical results highlight that the model should be further investigated for potential use in computer‐aided diagnoses to improve clinical decision‐making. |
| format | Article |
| id | doaj-art-432444d2a5b541a0986baec2d104b415 |
| institution | OA Journals |
| issn | 2162-3279 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj-art-432444d2a5b541a0986baec2d104b4152025-08-20T02:29:19ZengWileyBrain and Behavior2162-32792025-05-01155n/an/a10.1002/brb3.70520Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor ClassificationYasin Kaya0Ezgisu Akat1Serdar Yıldırım2Department of Artificial Intelligence Engineering Adana Alparslan Turkes Science and Technology University Adana TurkiyeDepartment of Computer Engineering Adana Alparslan Turkes Science and Technology University Adana TurkiyeDepartment of Computer Engineering Adana Alparslan Turkes Science and Technology University Adana TurkiyeABSTRACT Problem Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task. Aim This study aimed to develop a fusion model to address certain limitations of previous works, such as covering diverse image modalities in various datasets. Method We presented a hybrid transfer learning model, Fusion‐Brain‐Net, aimed at automatic brain tumor classification. The proposed method included four stages: preprocessing and data augmentation, fusion of deep feature extractions, fine‐tuning, and classification. Integrating the pre‐trained CNN models, VGG16, ResNet50, and MobileNetV2, the model enhanced comprehensive feature extraction while mitigating overfitting issues, improving the model's performance. Results The proposed model was rigorously tested and verified on four public datasets: Br35H, Figshare, Nickparvar, and Sartaj. It achieved remarkable accuracy rates of 99.66%, 97.56%, 97.08%, and 93.74%, respectively. Conclusion The numerical results highlight that the model should be further investigated for potential use in computer‐aided diagnoses to improve clinical decision‐making.https://doi.org/10.1002/brb3.70520brain tumor classificationfusion of CNNtransfer learning |
| spellingShingle | Yasin Kaya Ezgisu Akat Serdar Yıldırım Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification Brain and Behavior brain tumor classification fusion of CNN transfer learning |
| title | Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification |
| title_full | Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification |
| title_fullStr | Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification |
| title_full_unstemmed | Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification |
| title_short | Fusion‐Brain‐Net: A Novel Deep Fusion Model for Brain Tumor Classification |
| title_sort | fusion brain net a novel deep fusion model for brain tumor classification |
| topic | brain tumor classification fusion of CNN transfer learning |
| url | https://doi.org/10.1002/brb3.70520 |
| work_keys_str_mv | AT yasinkaya fusionbrainnetanoveldeepfusionmodelforbraintumorclassification AT ezgisuakat fusionbrainnetanoveldeepfusionmodelforbraintumorclassification AT serdaryıldırım fusionbrainnetanoveldeepfusionmodelforbraintumorclassification |