Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India.
Faculty of Engineering, Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda, Cuito-Bié, Angola.
Comput Math Methods Med. 2022 Aug 9;2022:8680737. doi: 10.1155/2022/8680737. eCollection 2022.
Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer's disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN's receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN's capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.
在当前十年中，医疗保健的发展引起了广泛的关注，尤其是它们为延长和改善个人寿命提供的服务。阿尔茨海默病（AD）是最常见的神经退行性疾病和导致痴呆的疾病。预计治疗 AD 患者的经济费用将会增加。因此，开发一种用于早期 AD 分类的计算机辅助技术变得更加必要。深度学习（DL）模型相对于机器学习工具具有许多优势。一些最新的实验利用脑磁共振成像（MRI）扫描和卷积神经网络（CNN）对 AD 进行分类，得出了有希望的结论。CNN 的感受野有助于从这些 MRI 扫描中提取主要可识别特征。为了提高分类准确性，本研究提出了一种基于 CNN 和支持向量机（SVM）的新自适应模型，结合了 CNN 在特征提取方面的能力和 SVM 在分类方面的能力。本研究的目的是利用 MRI ADNI 数据集构建一个用于 AD 分类的混合 CNN-SVM 模型。实验结果表明，混合 CNN-SVM 模型优于单独的 CNN 模型，在 AD 与认知正常（CN）、CN 与轻度认知障碍（MCI）、AD 与 MCI 以及 CN 与 MCI 与 AD 的测试数据集上，分别有 3.4%、1.09%、0.85%和 2.82%的相对改进。最后，该方法在 OASIS 数据集上进一步进行了实验，准确率达到 86.2%。
