Abstract
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.
Abstract (translated)
基于人工智能（AI）的深度学习在物联网（IoT）医疗系统中的脑肿瘤诊断方面取得了显著进展，利用大规模数据集实现了高精度。大脑健康对人类生命至关重要，准确的诊断对于有效的治疗是必不可少的。磁共振成像（MRI）提供了脑肿瘤检测的关键数据，并且作为人工智能驱动图像分类的大数据的主要来源。在这项研究中，我们使用Region-based Convolutional Neural Network (R-CNN) 和 UNet 架构从MRI影像中对胶质瘤、脑膜瘤和垂体瘤进行了分类。同时，我们也应用了基于卷积神经网络（CNN）的模型以及诸如Inception-V3, EfficientNetB4 和 VGG19 的迁移学习模型。通过F值、召回率、准确率等指标来评估模型性能。Fast R-CNN 模型取得了最佳效果，其精度达到了99%，F值为98.5%，曲线下面积（AUC）为99.5%，召回率为99.4%，准确率为98.5%。 将R-CNN、UNet和迁移学习结合起来可以实现在物联网医疗系统中更早的诊断和更有成效的治疗，从而改善患者预后。物联网设备如可穿戴监测器和智能成像系统会持续收集实时数据，AI算法分析这些数据以提供即时洞察并进行及时干预和个人化护理。 在外部队列跨数据集验证方面，经过微调后的EfficientNetB2模型表现出色，在精度、召回率/灵敏度、特异性、F1得分和准确度上分别达到了92.11%、92.11%、95.96%、92.02% 和 92.23%。这些研究结果强调了AI模型处理多样化数据集的稳健性和可靠性，进一步强化了它们在物联网医疗环境中增强脑肿瘤分类和患者护理方面的潜力。
URL
https://arxiv.org/abs/2509.05821
