Chen Wenna, Liu Junqiang, Tan Xinghua, Zhang Jincan, Du Ganqin, Fu Qizhi, Jiang Hongwei
The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
College of Information Engineering, Henan University of Science and Technology, Luoyang, China.
Front Oncol. 2025 Apr 11;15:1512739. doi: 10.3389/fonc.2025.1512739. eCollection 2025.
Brain tumors pose significant harm to the functionality of the human nervous system. There are lots of models which can classify brain tumor type. However, the available methods did not pay special attention to long-range information, which limits model accuracy improvement.
To solve this problem, in this paper, an enhanced short-range and long-range dependent system for brain tumor classification, named as EnSLDe, is proposed. The EnSLDe model consists of three main modules: the Feature Extraction Module (FExM), the Feature Enhancement Module (FEnM), and the Classification Module. Firstly, the FExM is used to extract features and the multi-scale parallel subnetwork is constructed to fuse shallow and deep features. Then, the extracted features are enhanced by the FEnM. The FEnM can capture the important dependencies across a larger sequence range and retain critical information at a local scale. Finally, the fused and enhanced features are input to the classification module for brain tumor classification. The combination of these modules enables the efficient extraction of both local and global contextual information.
In order to validate the model, two public data sets including glioma, meningioma, and pituitary tumor were validated, and good experimental results were obtained, demonstrating the potential of the model EnSLDe in brain tumor classification.
脑肿瘤对人类神经系统的功能造成严重损害。有许多模型可以对脑肿瘤类型进行分类。然而，现有的方法没有特别关注长程信息，这限制了模型准确性的提高。
为了解决这个问题，本文提出了一种用于脑肿瘤分类的增强型短程和长程依赖系统，称为EnSLDe。EnSLDe模型由三个主要模块组成：特征提取模块（FExM）、特征增强模块（FEnM）和分类模块。首先，FExM用于提取特征，并构建多尺度并行子网以融合浅层和深层特征。然后，提取的特征由FEnM增强。FEnM可以在更大的序列范围内捕获重要的依赖关系，并在局部尺度上保留关键信息。最后，将融合和增强后的特征输入到分类模块中进行脑肿瘤分类。这些模块的组合能够有效地提取局部和全局上下文信息。
为了验证该模型，对包括胶质瘤、脑膜瘤和垂体瘤在内的两个公共数据集进行了验证，并获得了良好的实验结果，证明了EnSLDe模型在脑肿瘤分类中的潜力。
