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Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

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Fusion-Brain-Net: A Novel Deep Fusion Model for Brain Tumor Classification.

Yasin Kaya¹, Ezgisu Akat², Serdar Yıldırım²

¹Department of Artificial Intelligence Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkiye.

Brain and Behavior

|May 9, 2025

Summary

This study introduces Fusion-Brain-Net, a hybrid transfer learning model for automatic brain tumor classification. The model achieves high accuracy across diverse datasets, offering potential for improved computer-aided diagnosis.

Keywords:

brain tumor classification fusion of CNN transfer learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Brain tumors represent a significant global health challenge, demanding accurate and timely diagnosis.
Manual classification of brain tumors is time-consuming and prone to errors.
Existing automated methods often struggle with diverse image modalities and datasets.

Purpose of the Study:

To develop an advanced fusion model for automated brain tumor classification.
To overcome limitations of previous models in handling varied data.
To enhance the accuracy and efficiency of brain tumor detection.

Main Methods:

A hybrid transfer learning approach, Fusion-Brain-Net, was developed.
The model integrates pre-trained Convolutional Neural Network (CNN) models: VGG16, ResNet50, and MobileNetV2.
Key stages include preprocessing, data augmentation, deep feature fusion, fine-tuning, and classification.

Main Results:

Fusion-Brain-Net demonstrated high performance on four public datasets.
Accuracy rates achieved were 99.66% (Br35H), 97.56% (Figshare), 97.08% (Nickparvar), and 93.74% (Sartaj).
The model effectively extracted comprehensive features and mitigated overfitting.

Conclusions:

The developed Fusion-Brain-Net model shows significant promise for automated brain tumor classification.
Further investigation is warranted for its application in computer-aided diagnosis systems.
The model has the potential to enhance clinical decision-making processes.