This study proposes an efficient Densely Swin Hybrid (EDSH) framework for brain tumor MRI analysis, designed to jointly capture fine grained texture patterns and long range contextual dependencies. Two tumor aware experimental setups are introduced to address class-specific diagnostic challenges. The first setup employs a Boosted Feature Space (BFS), where independently customized DenseNet and Swint branches learn complementary local and global representations that are dimension aligned, fused, and boosted, enabling highly sensitive detection of diffuse glioma patterns by successfully learning the features of irregular shape, poorly defined mass, and heterogeneous texture. The second setup adopts a hierarchical DenseNet Swint architecture with Deep Feature Extraction have Dual Residual connections (DFE and DR), in which DenseNet serves as a stem CNN for structured local feature learning, while Swin_t models global tumor morphology, effectively suppressing false negatives in meningioma and pituitary tumor classification by learning the features of well defined mass, location (outside brain) and enlargments in tumors (dural tail or upward extension). DenseNet is customized at the input level to match MRI spatial characteristics, leveraging dense residual connectivity to preserve texture information and mitigate vanishing-gradient effects. In parallel, Swint is tailored through task aligned patch embedding and shifted-window self attention to efficiently capture hierarchical global dependencies. Extensive evaluation on a large-scale MRI dataset (stringent 40,260 images across four tumor classes) demonstrates consistent superiority over standalone CNNs, Vision Transformers, and hybrids, achieving 98.50 accuracy and recall on the test unseen dataset.

翻译：本研究提出了一种高效的密集Swin混合（EDSH）框架，用于脑肿瘤MRI分析，旨在同时捕捉细粒度纹理模式和长距离上下文依赖关系。为应对特定类别的诊断挑战，引入了两种肿瘤感知实验设置。第一种设置采用增强特征空间（BFS），其中独立定制的DenseNet和Swin分支学习互补的局部与全局表示，这些表示经过维度对齐、融合和增强，通过成功学习不规则形状、边界模糊肿块和异质性纹理的特征，实现对弥漫性胶质瘤模式的高灵敏度检测。第二种设置采用具有深度特征提取及双重残差连接（DFE与DR）的分层DenseNet-Swin架构，其中DenseNet作为主干CNN进行结构化局部特征学习，而Swin_t模型则建模全局肿瘤形态，通过学习边界清晰肿块、位置（脑外）及肿瘤扩大（硬膜尾征或向上延伸）等特征，有效抑制脑膜瘤和垂体瘤分类中的假阴性。DenseNet在输入层针对MRI空间特性进行定制，利用密集残差连接保持纹理信息并缓解梯度消失效应。同时，Swin_t通过任务对齐的补丁嵌入和移位窗口自注意力机制进行适配，以高效捕捉分层的全局依赖关系。在大规模MRI数据集（涵盖四类肿瘤的严格40,260张图像）上的广泛评估表明，该框架始终优于独立CNN、视觉Transformer及混合模型，在未见测试数据集上实现了98.50%的准确率与召回率。