PDF(Pubmed)
Abstract：
The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently.
摘要：
磁共振成像(MRI)在脑肿瘤分类中的应用受到传统诊断程序复杂、耗时的制约,主要是因为需要对几个地区进行全面评估。然而，深度学习（DL）的进步促进了自动化系统的开发，该系统可以改善医学图像的识别和评估，有效应对这些困难。卷积神经网络（CNN）已经成为图像分类和视觉感知的坚定工具。这项研究引入了一种创新的方法，将CNN与混合注意力机制相结合，对原发性脑肿瘤进行分类，包括神经胶质瘤,脑膜瘤,垂体,和无肿瘤病例。所提出的算法经过了来自文献中有据可查的基准数据的严格测试。它与建立的预训练模型如Xception、ResNet50V2、Densenet201、ResNet101V2和DenseNet169。该方法的性能指标显著，分类准确率为98.33%，准确率和召回率为98.30%，F1评分为98.20%。实验发现强调了新方法在识别最常见类型的脑肿瘤方面的优越性。此外，该方法表现出良好的泛化能力，使其成为医疗保健准确有效地诊断大脑状况的宝贵工具。
