Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, 01250, Turkey.
Department of Artificial Intelligence Engineering, Adana Alparslan Turkes Science and Technology University, Adana, 01250, Turkey.
Comput Biol Med. 2024 Sep;180:108971. doi: 10.1016/j.compbiomed.2024.108971. Epub 2024 Aug 5.
The intersection of artificial intelligence and medical image analysis has ushered in a new era of innovation and changed the landscape of brain tumor detection and diagnosis. Correct detection and classification of brain tumors based on medical images is crucial for early diagnosis and effective treatment. Convolutional Neural Network (CNN) models are widely used for disease detection. However, they are sometimes unable to sufficiently recognize the complex features of medical images.
This paper proposes a fused Deep Learning (DL) model that combines Graph Neural Networks (GNN), which recognize relational dependencies of image regions, and CNN, which captures spatial features, is proposed to improve brain tumor detection. By integrating these two architectures, our model achieves a more comprehensive representation of brain tumor images and improves classification performance. The proposed model is evaluated on a public dataset of 10847 MRI images. The results show that the proposed model outperforms the existing pre-trained models and traditional CNN architectures.
The fused DL model achieves 93.68% accuracy in brain tumor classification. The results indicate that the proposed model outperforms the existing pre-trained models and traditional CNN architectures.
The numerical results suggest that the model should be further investigated for potential use in clinical trials to improve clinical decision-making.
人工智能与医学图像分析的交叉融合开创了创新的新纪元，改变了脑肿瘤检测和诊断的格局。基于医学图像对脑肿瘤进行正确的检测和分类，对于早期诊断和有效治疗至关重要。卷积神经网络（CNN）模型广泛应用于疾病检测。然而，它们有时无法充分识别医学图像的复杂特征。
本文提出了一种融合深度学习（DL）模型，该模型结合了图神经网络（GNN），可以识别图像区域的关系依赖关系，以及卷积神经网络（CNN），可以捕获空间特征，用于提高脑肿瘤检测。通过整合这两种架构，我们的模型实现了对脑肿瘤图像更全面的表示，并提高了分类性能。该模型在一个包含 10847 个 MRI 图像的公共数据集上进行了评估。结果表明，所提出的模型优于现有的预训练模型和传统的 CNN 架构。
融合的 DL 模型在脑肿瘤分类中达到了 93.68%的准确率。结果表明，所提出的模型优于现有的预训练模型和传统的 CNN 架构。
数值结果表明，该模型应进一步研究，以潜在地用于临床试验，从而改善临床决策。
