提出新型混合模型，精准识别脑肿瘤MRI图像中的细微差异。
CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis
- 融合残差、空间与边界感知学习的混合网络架构。
- 在多个数据集上达到98.3%准确率，显著优于现有方法。
- 适合医学影像分析、脑肿瘤诊断等临床辅助场景使用。
脑肿瘤是致命性人类疾病之一，早期检测与准确分类对诊疗至关重要。尽管基于深度学习的计算机辅助诊断系统已取得显著进展，但传统卷积神经网络（CNN）与Transformer仍面临计算成本高、对微小对比度变化敏感、结构异质性及纹理不一致等问题。为此，本文提出一种新型混合框架CE-RS-SBCIT，结合残差与空间学习的CNN和Transformer驱动模块。该框架通过四项创新实现：(i) 平滑与边界感知的CNN-Transformer融合模块（SBCIT），(ii) 定制化残差与空间学习CNN，(iii) 通道增强（CE）策略，(iv) 新型空间注意力机制。SBCIT采用茎部卷积与上下文交互变压器块，配合系统性平滑与边界操作，实现高效全局特征建模；残差与空间CNN通过辅助迁移特征图增强表征能力；CE模块放大判别性通道并抑制冗余；空间注意力机制聚焦于不同肿瘤类别间的细微对比与纹理差异。在Kaggle与Figshare的挑战性MRI数据集（包含胶质瘤、脑膜瘤、垂体瘤及健康对照）上广泛评估，性能卓越，达98.30%准确率、98.08%灵敏度、98.25%F1分数与98.43%精确率。
原文摘要 · Abstract (English)
Brain tumors remain among the most lethal human diseases, where early detection and accurate classification are critical for effective diagnosis and treatment planning. Although deep learning-based computer-aided diagnostic (CADx) systems have shown remarkable progress. However, conventional convolutional neural networks (CNNs) and Transformers face persistent challenges, including high computational cost, sensitivity to minor contrast variations, structural heterogeneity, and texture inconsistencies in MRI data. Therefore, a novel hybrid framework, CE-RS-SBCIT, is introduced, integrating residual and spatial learning-based CNNs with transformer-driven modules. The proposed framework exploits local fine-grained and global contextual cues through four core innovations: (i) a smoothing and boundary-based CNN-integrated Transformer (SBCIT), (ii) tailored residual and spatial learning CNNs, (iii) a channel enhancement (CE) strategy, and (iv) a novel spatial attention mechanism. The developed SBCIT employs stem convolution and contextual interaction transformer blocks with systematic smoothing and boundary operations, enabling efficient global feature modeling. Moreover, Residual and spatial CNNs, enhanced by auxiliary transfer-learned feature maps, enrich the representation space, while the CE module amplifies discriminative channels and mitigates redundancy. Furthermore, the spatial attention mechanism selectively emphasizes subtle contrast and textural variations across tumor classes. Extensive evaluation on challenging MRI datasets from Kaggle and Figshare, encompassing glioma, meningioma, pituitary tumors, and healthy controls, demonstrates superior performance, achieving 98.30% accuracy, 98.08% sensitivity, 98.25% F1-score, and 98.43% precision.
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