用改进的ResNet50模型和新数据集，提升脑瘤MRI自动识别准确率。
Brain Tumor Classification from MRI Scans via Transfer Learning and Enhanced Feature Representation
- 基于预训练ResNet50提取特征，结合新型密集丢弃层增强非线性学习。
- 在3671张肿瘤图像上达到98.4%分类准确率，显著优于基线方法。
- 自建MMCBT数据集，适合医疗影像深度学习研究，尤其关注脑瘤检测。
脑肿瘤是中枢神经系统异常细胞增殖，及时检测对改善患者预后至关重要。本文提出一种自动高效的深度学习框架，用于从磁共振成像（MRI）扫描中检测脑肿瘤。框架采用预训练的ResNet50模型进行特征提取，随后通过全局平均池化（GAP）和线性投影获得紧凑的高层次图像表征。这些特征经由一种新型的密集-丢弃序列处理，该序列是本文的核心贡献，通过多样化的特征变换增强了非线性特征学习能力，减少了过拟合，并提升了鲁棒性。另一重要贡献是构建了孟加拉国米姆宁斯医疗学院脑肿瘤（MMCBT）数据集，以解决可靠脑肿瘤MRI资源不足的问题。该数据集包含209名受试者（年龄9至65岁）的MRI扫描，涵盖3671张肿瘤图像和13273张非肿瘤图像，均经专家监督临床验证。为缓解类别不平衡问题，对肿瘤类别进行了数据增强，得到一个平衡的数据集，适用于深度学习研究。
原文摘要 · Abstract (English)
Brain tumors are abnormal cell growths in the central nervous system (CNS), and their timely detection is critical for improving patient outcomes. This paper proposes an automatic and efficient deep-learning framework for brain tumor detection from magnetic resonance imaging (MRI) scans. The framework employs a pre-trained ResNet50 model for feature extraction, followed by Global Average Pooling (GAP) and linear projection to obtain compact, high-level image representations. These features are then processed by a novel Dense-Dropout sequence, a core contribution of this work, which enhances non-linear feature learning, reduces overfitting, and improves robustness through diverse feature transformations. Another major contribution is the creation of the Mymensingh Medical College Brain Tumor (MMCBT) dataset, designed to address the lack of reliable brain tumor MRI resources. The dataset comprises MRI scans from 209 subjects (ages 9 to 65), including 3671 tumor and 13273 non-tumor images, all clinically verified under expert supervision. To overcome class imbalance, the tumor class was augmented, resulting in a balanced dataset well-suited for deep learning research.
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