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 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.