Kaya, YasinAkat, EzgisuYildirim, Serdar2026-02-272026-02-2720252162-327910.1002/brb3.70520http://dx.doi.org/10.1002/brb3.70520https://hdl.handle.net/20.500.14669/4397Problem: 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.eninfo:eu-repo/semantics/openAccessbrain tumor classificationfusion of CNNtransfer learningArticle54034182815WOS:001484364400001