用YOLOv8和YOLOv11模型实现脑肿瘤快速精准分类，提升MRI诊断效率。
Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models
- 基于迁移学习微调YOLOv8和YOLOv11模型进行四类脑组织分类
- 在CE-MRI Figshare数据集上达到99.49%与99.56%的准确率
- 适合医学影像分析、AI辅助诊断系统开发者参考
准确快速诊断正常脑组织及胶质瘤、脑膜瘤、垂体瘤对优化治疗方案和改善医疗结果至关重要。磁共振成像（MRI）是检测脑部异常（包括肿瘤）的常用非侵入性工具，但人工解读MRI耗时长、易出错且依赖专业经验。本文提出一种基于YOLOv11和YOLOv8深度学习模型的AI驱动技术，用于检测并分类胶质瘤、脑膜瘤、垂体瘤及正常脑组织。采用迁移学习微调策略，将先进深度学习方法与医学影像结合，实现四类分类：无肿瘤、胶质瘤、脑膜瘤、垂体瘤。实验使用公开可获取的CE-MRI Figshare数据集，微调后的YOLOv8和YOLOv11模型分别达到99.49%和99.56%的准确率，自定义CNN模型达96.98%。结果验证了卷积神经网络在脑肿瘤检测中高精度的潜力，凸显其在医学影像诊断中的变革作用。
原文摘要 · Abstract (English)
Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuracies; and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics.
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