Abstract
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.
Abstract (translated)
脑肿瘤是中枢神经系统（CNS）中的异常细胞生长，其及时检测对于改善患者预后至关重要。本文提出了一种自动且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。该框架采用预训练的ResNet50模型进行特征提取，并通过全局平均池化（GAP）和线性投影来获得紧凑、高层次的图像表示。这些特征随后由一种新颖的Dense-Dropout序列处理，这是本文的核心贡献之一，它增强了非线性特征学习能力，减少了过拟合，并通过多样化的特征转换提高了模型的鲁棒性。 另一个重大贡献是创建了梅蒙辛格医学院脑肿瘤（MMCBT）数据集，旨在解决可靠脑肿瘤MRI资源缺乏的问题。该数据集包括209名受试者（年龄范围为9至65岁）的MRI扫描图像，其中包括3671张肿瘤影像和13273张非肿瘤影像，所有影像均在专家监督下临床验证过。为了克服类别不平衡问题，对肿瘤类进行了增强处理，从而生成了一个适合深度学习研究的平衡数据集。
URL
https://arxiv.org/abs/2509.22956
