Abstract：
The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model\'s performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model\'s innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.
摘要：
医学图像分析（MI）是先进医学的重要组成部分，因为它有助于早期发现和诊断各种疾病。通过磁共振成像（MRI）对脑肿瘤进行分类是一项挑战，需要准确的模型来进行有效的诊断和治疗计划。本文介绍了AG-MSTLN-EL,利用多源迁移学习的注意力辅助多源迁移学习集成学习模型（VisualGeometryGroupResNet和GoogLeNet），注意机制，和集成学习，以实现健壮和准确的脑肿瘤分类。多源迁移学习允许从不同领域提取知识，增强泛化。注意机制集中在特定的MRI区域，提高可解释性和分类性能。集成学习结合了k-最近邻，Softmax,和支持向量机分类器，提高准确性和可靠性。在具有3064个脑肿瘤MRI图像的数据集上评估模型的性能，AG-MSTLN-EL在所有分类措施方面都优于最先进的模型。迁移学习模式的创新组合,注意机制，集成学习为脑肿瘤分类提供了可靠的解决方案。其卓越的性能和高解释性使AG-MSTLN-EL成为临床医生和研究人员在医学图像分析中的宝贵工具。
