用深度学习融合定位与分类，提升脑瘤MRI诊断准确率至99.86%
Accelerating Cerebral Diagnostics with BrainFusion: A Comprehensive MRI Tumor Framework
- 用微调的VGG16+YOLOv8实现肿瘤分类与精确定位
- 在Brain Tumor MRI数据集上达99.86%测试准确率
- 结合可解释AI，增强临床可信度，适合医疗AI研发者
早期精准分类脑瘤对指导治疗策略、改善患者预后至关重要。本文提出BrainFusion，通过融合微调的卷积神经网络（包括VGG16、ResNet50和Xception）进行肿瘤分类，同时采用YOLOv8实现肿瘤精确定位（边界框）。基于Brain Tumor MRI数据集的实验表明，微调后的VGG16模型测试准确率达99.86%，显著超越以往基准。该系统不仅创下新准确率标准，还结合边界框定位与可解释AI技术，进一步提升结果的临床可解释性与可信度。整体上，该方法展示了深度学习在加速可靠诊断中的变革潜力，有助于提升患者护理质量与生存率。
原文摘要 · Abstract (English)
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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