Kaya Yasin, Akat Ezgisu, Yıldırım Serdar
Department of Artificial Intelligence Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkiye.
Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkiye.
Brain Behav. 2025 May;15(5):e70520. doi: 10.1002/brb3.70520.
Brain tumors are among the most prevalent and lethal diseases. Early diagnosis and precise treatment are crucial. However, the manual classification of brain tumors is a laborious and complex task.
This study aimed to develop a fusion model to address certain limitations of previous works, such as covering diverse image modalities in various datasets.
We presented a hybrid transfer learning model, Fusion-Brain-Net, aimed at automatic brain tumor classification. The proposed method included four stages: preprocessing and data augmentation, fusion of deep feature extractions, fine-tuning, and classification. Integrating the pre-trained CNN models, VGG16, ResNet50, and MobileNetV2, the model enhanced comprehensive feature extraction while mitigating overfitting issues, improving the model's performance.
The proposed model was rigorously tested and verified on four public datasets: Br35H, Figshare, Nickparvar, and Sartaj. It achieved remarkable accuracy rates of 99.66%, 97.56%, 97.08%, and 93.74%, respectively.
The numerical results highlight that the model should be further investigated for potential use in computer-aided diagnoses to improve clinical decision-making.
脑肿瘤是最常见且致命的疾病之一。早期诊断和精准治疗至关重要。然而，脑肿瘤的人工分类是一项艰巨且复杂的任务。
本研究旨在开发一种融合模型，以解决先前研究的某些局限性，比如涵盖各种数据集中的不同图像模态。
我们提出了一种混合迁移学习模型，即融合脑网络（Fusion - Brain - Net），用于自动脑肿瘤分类。所提出的方法包括四个阶段：预处理和数据增强、深度特征提取融合、微调以及分类。该模型整合了预训练的卷积神经网络（CNN）模型VGG16、ResNet50和MobileNetV2，在增强综合特征提取的同时减轻了过拟合问题，提升了模型性能。
所提出的模型在四个公共数据集（Br35H、Figshare、Nickparvar和Sartaj）上进行了严格测试和验证。它分别取得了99.66%、97.56%、97.08%和93.74%的显著准确率。
数值结果表明，该模型应进一步研究其在计算机辅助诊断中的潜在用途，以改善临床决策。
