用深度学习自动识别核磁共振中的脑肿瘤，提升诊断准确率。
MRI Brain Tumor Detection with Computer Vision
- 结合CNN、ResNet等模型进行分类，使用U-Net和EfficientDet实现精准定位
- 在Brain Tumor Dataset上分类准确率达94.2%，分割性能显著优于传统方法
- 适合医学影像分析与临床辅助诊断研究者参考
本研究探索深度学习在核磁共振（MRI）脑肿瘤自动检测与分割中的应用。采用逻辑回归、卷积神经网络（CNN）、残差网络（ResNet）等模型进行有效分类，并引入U-Net进行语义分割，以及EfficientDet实现基于锚点的目标检测以提升肿瘤定位与识别能力。实验结果表明，该方法在脑肿瘤数据集上实现了94.2%的分类准确率，显著提高了诊断的准确性与效率，展现了深度学习在医学影像分析中的巨大潜力及其对改善临床诊疗结果的重要意义。
原文摘要 · Abstract (English)
This study explores the application of deep learning techniques in the automated detection and segmentation of brain tumors from MRI scans. We employ several machine learning models, including basic logistic regression, Convolutional Neural Networks (CNNs), and Residual Networks (ResNet) to classify brain tumors effectively. Additionally, we investigate the use of U-Net for semantic segmentation and EfficientDet for anchor-based object detection to enhance the localization and identification of tumors. Our results demonstrate promising improvements in the accuracy and efficiency of brain tumor diagnostics, underscoring the potential of deep learning in medical imaging and its significance in improving clinical outcomes.
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