Rasheed Zahid, Ma Yong-Kui, Ullah Inam, Al-Khasawneh Mahmoud, Almutairi Sulaiman Sulmi, Abohashrh Mohammed
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongman 13120, Republic of Korea.
Bioengineering (Basel). 2024 Jul 10;11(7):701. doi: 10.3390/bioengineering11070701.
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.
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且耗时特性的限制，主要原因是需要对多个区域进行全面评估。然而，深度学习（DL）的进步推动了一个自动化系统的开发，该系统改善了医学图像的识别和评估，有效解决了这些难题。卷积神经网络（CNN）已成为图像分类和视觉感知的可靠工具。本研究引入了一种将CNN与混合注意力机制相结合的创新方法，用于对原发性脑肿瘤进行分类，包括胶质瘤、脑膜瘤、垂体瘤和无肿瘤病例。所提出的算法使用文献中记录良好来源的基准数据进行了严格测试。它与诸如Xception、ResNet50V2、Densenet201、ResNet101V2和DenseNet169等已建立的预训练模型一起进行了评估。所提出方法的性能指标非常出色，分类准确率为98.33%，精确率和召回率为98.30%，F1分数为98.20%。实验结果突出了新方法在识别最常见脑肿瘤类型方面的卓越性能。此外，该方法显示出出色的泛化能力，使其成为医疗保健领域准确高效诊断脑部疾病的宝贵工具。
