Accelerating Cerebral Diagnostics with BrainFusion: A Comprehensive MRI Tumor Framework

• URL: http://arxiv.org/abs/2509.24149v1
• Date: Mon, 29 Sep 2025 00:53:17 GMT
• Title: Accelerating Cerebral Diagnostics with BrainFusion: A Comprehensive MRI Tumor Framework
• Authors: Walid Houmaidi, Youssef Sabiri, Salmane El Mansour Billah, Amine Abouaomar,
• Abstract summary: This study presents BrainFusion, a significant advancement in brain tumor analysis using magnetic resonance imaging (MRI)<n>We combine fine-tuned convolutional neural networks (CNNs) for tumor classification--including VGG16, ResNet50, and Xception--with YOLOv8 for precise tumor localization with bounding boxes.<n>Our experiments reveal that the fine-tuned VGG16 model achieves test accuracy of 99.86%, substantially exceeding previous benchmarks.
• Score: 0.41998444721319217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The early and accurate classification of brain tumors is crucial for guiding effective treatment strategies and improving patient outcomes. This study presents BrainFusion, a significant advancement in brain tumor analysis using magnetic resonance imaging (MRI) by combining fine-tuned convolutional neural networks (CNNs) for tumor classification--including VGG16, ResNet50, and Xception--with YOLOv8 for precise tumor localization with bounding boxes. Leveraging the Brain Tumor MRI Dataset, our experiments reveal that the fine-tuned VGG16 model achieves test accuracy of 99.86%, substantially exceeding previous benchmarks. Beyond setting a new accuracy standard, the integration of bounding-box localization and explainable AI techniques further enhances both the clinical interpretability and trustworthiness of the system's outputs. Overall, this approach underscores the transformative potential of deep learning in delivering faster, more reliable diagnoses, ultimately contributing to improved patient care and survival rates.

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