用深度学习自动识别MRI中的脑肿瘤，准确率达97%。
Automated brain tumor detection in MRI images using CNN and ResNet architectures
- 用ResNet18和ResNet50做迁移学习，从MRI图像分类是否含肿瘤。
- 在3929张图像上，ResNet18准确率97%，优于ResNet50的96%。
- 适合临床辅助诊断，尤其在医疗数据有限时表现更稳。
深度学习在医学图像分析中展现出巨大潜力，尤其在利用MRI扫描进行疾病检测方面。由于大脑结构复杂且依赖人工解读，脑肿瘤的准确和早期诊断仍具挑战。本文提出一种基于深度学习的自动化方法，使用卷积神经网络（CNN）和残差网络（ResNet）从MRI图像中检测脑肿瘤。采用两种预训练模型——ResNet18和ResNet50——进行迁移学习，将MRI图像分为肿瘤与非肿瘤两类。实验基于包含3,929张脑部MRI图像的数据集，评估模型深度与微调策略的影响。结果表明，ResNet18达到97%的准确率，高于ResNet50的96%，在有限医疗数据下表现出更好的泛化能力。所提框架实现了快速、准确且低成本的脑肿瘤检测，有助于早期诊断与临床决策。
原文摘要 · Abstract (English)
Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans. Accurate and early diagnosis of brain tumors remains challenging due to the complexity of brain structures and reliance on manual interpretation. This work presents an automated deep learning-based approach for brain tumor detection from MRI images using Convolutional Neural Networks and Residual Networks. Transfer learning is applied with two pretrained architectures, ResNet18 and ResNet50, to classify MRI scans into tumor and non-tumor categories. Experiments are conducted on a dataset of 3,929 brain MRI images, evaluating the impact of model depth and fine-tuning strategies. The results show that ResNet18 achieves a higher accuracy of 97% compared to 96% for ResNet50, demonstrating better generalization on limited medical data. The proposed framework enables fast, accurate, and cost-effective brain tumor detection, supporting early diagnosis and clinical decision-making.
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