Accelerating Cerebral Diagnostics with BrainFusion: A Comprehensive MRI Tumor Framework

2025-09-28

AI Summary

To address the clinical need for early, precise classification of brain tumors, this study proposes an interpretable deep learning framework that jointly performs tumor localization and classification. Methodologically, YOLOv8 is employed for pixel-accurate tumor localization in MRI scans, while fine-tuned VGG16, ResNet50, and Xception networks enable fine-grained tumor subtype classification. Gradient-weighted Class Activation Mapping (Grad-CAM) and other explainable AI techniques are integrated to enhance clinical interpretability and decision transparency. Evaluated on the Brain Tumor MRI Dataset, the framework achieves a test accuracy of 99.86%, substantially outperforming existing benchmarks, while delivering high-precision bounding-box localization and human-readable visual explanations of model decisions. The primary contributions are: (i) the first synergistic integration of YOLOv8 with multiple CNN-based classifiers for brain tumor MRI analysis; and (ii) a systematic incorporation of explainability modules, thereby balancing diagnostic efficiency, classification accuracy, and clinical trustworthiness.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Early and accurate brain tumor classification for treatment guidance
• Integrating CNN classification with YOLOv8 for precise tumor localization
• Enhancing clinical interpretability using explainable AI techniques

Innovation

• Fine-tuned CNNs for brain tumor classification
• YOLOv8 for precise tumor localization
• Explainable AI techniques for clinical interpretability