Alhudhaif Adi
Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia.
PeerJ Comput Sci. 2024 Feb 27;10:e1862. doi: 10.7717/peerj-cs.1862. eCollection 2024.
Artificial intelligence technologies have great potential in classifying neurodegenerative diseases such as Alzheimer's and Parkinson's. These technologies can aid in early diagnosis, enhance classification accuracy, and improve patient access to appropriate treatments. For this purpose, we focused on AI-based auto-diagnosis of Alzheimer's disease, Parkinson's disease, and healthy MRI images.
In the current study, a deep hybrid network based on an ensemble classifier and convolutional neural network was designed. First, a very deep super-resolution neural network was adapted to improve the resolution of MRI images. Low and high-level features were extracted from the images processed with the hybrid deep convolutional neural network. Finally, these deep features are given as input to the k-nearest neighbor (KNN)-based random subspace ensemble classifier.
A 3-class dataset containing publicly available MRI images was utilized to test the proposed architecture. In experimental works, the proposed model produced 99.11% accuracy, 98.75% sensitivity, 99.54% specificity, 98.65% precision, and 98.70% F1-score performance values. The results indicate that our AI system has the potential to provide valuable diagnostic assistance in clinical settings.
人工智能技术在诸如阿尔茨海默病和帕金森病等神经退行性疾病的分类方面具有巨大潜力。这些技术有助于早期诊断，提高分类准确性，并改善患者获得适当治疗的机会。为此，我们专注于基于人工智能的阿尔茨海默病、帕金森病和健康MRI图像的自动诊断。
在当前研究中，设计了一种基于集成分类器和卷积神经网络的深度混合网络。首先，采用一个非常深的超分辨率神经网络来提高MRI图像的分辨率。从经过混合深度卷积神经网络处理的图像中提取低级和高级特征。最后，将这些深度特征作为基于k近邻（KNN）的随机子空间集成分类器的输入。
利用一个包含公开可用MRI图像的3类数据集来测试所提出的架构。在实验工作中，所提出的模型产生了99.11%的准确率、98.75%的灵敏度、99.54%的特异性、98.65%的精确率和98.70%的F1分数性能值。结果表明，我们的人工智能系统有潜力在临床环境中提供有价值的诊断辅助。
