用多种CNN模型提升IoT医疗中脑瘤检测精度，最快识别率达99%。
Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
- 融合Fast R-CNN、U-Net与迁移学习模型进行MRI图像分类
- Fast R-CNN实现99%准确率，AUC达99.5%
- 适用于智能医疗设备实时诊断，助力早期干预
人工智能驱动的深度学习已推动物联网医疗系统中脑肿瘤诊断的进步，在大规模数据集上实现高精度。磁共振成像（MRI）为脑瘤检测提供关键数据，是AI图像分类的主要大数据来源。本研究利用基于区域的卷积神经网络（R-CNN）和U-Net架构，对胶质瘤、脑膜瘤和垂体瘤的MRI图像进行分类。同时采用传统CNN及基于迁移学习的模型，如Inception-V3、EfficientNetB4和VGG19。模型性能通过F-score、召回率、精确率和准确率评估。Fast R-CNN表现最佳，准确率达99%，F-score为98.5%，曲线下面积（AUC）为99.5%，召回率为99.4%，精确率为98.5%。结合R-CNN、U-Net与迁移学习可提升物联网医疗系统中的早期诊断与治疗效果。可穿戴监测设备与智能成像系统持续采集实时数据，由AI算法分析以实现即时洞察与个性化诊疗。外部队列跨数据集验证中，微调后的EfficientNetB2表现最优，精确率92.11%，召回率/敏感度92.11%，特异度95.96%，F1-score为92.02%，准确率92.23%。结果表明，AI模型在处理多样化数据集时具备强鲁棒性与可靠性，彰显其在物联网医疗环境中提升脑瘤分类与患者护理的潜力。
原文摘要 · Abstract (English)
Artificial intelligence (AI)-powered deep learning has advanced brain tumor diagnosis in Internet of Things (IoT)-healthcare systems, achieving high accuracy with large datasets. Brain health is critical to human life, and accurate diagnosis is essential for effective treatment. Magnetic Resonance Imaging (MRI) provides key data for brain tumor detection, serving as a major source of big data for AI-driven image classification. In this study, we classified glioma, meningioma, and pituitary tumors from MRI images using Region-based Convolutional Neural Network (R-CNN) and UNet architectures. We also applied Convolutional Neural Networks (CNN) and CNN-based transfer learning models such as Inception-V3, EfficientNetB4, and VGG19. Model performance was assessed using F-score, recall, precision, and accuracy. The Fast R-CNN achieved the best results with 99% accuracy, 98.5% F-score, 99.5% Area Under the Curve (AUC), 99.4% recall, and 98.5% precision. Combining R-CNN, UNet, and transfer learning enables earlier diagnosis and more effective treatment in IoT-healthcare systems, improving patient outcomes. IoT devices such as wearable monitors and smart imaging systems continuously collect real-time data, which AI algorithms analyze to provide immediate insights for timely interventions and personalized care. For external cohort cross-dataset validation, EfficientNetB2 achieved the strongest performance among fine-tuned EfficientNet models, with 92.11% precision, 92.11% recall/sensitivity, 95.96% specificity, 92.02% F1-score, and 92.23% accuracy. These findings underscore the robustness and reliability of AI models in handling diverse datasets, reinforcing their potential to enhance brain tumor classification and patient care in IoT healthcare environments.
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