Alzheimer's disease is a progressive neurodegenerative fatal disease characterized by a decrease in mental functions. Although there is no definitive treatment for the disease, there are some treatment methods that delay the course of the disease in case of early diagnosis. Therefore, early diagnosis and classification of the disease are important to determine the most appropriate treatment. The most commonly used method for imaging the brain with a high soft-tissue resolution is magnetic resonance imaging (MRI). Brain MRI help in the diagnosis of Alzheimer's disease with some specific imaging findings. In this study, we aimed to classify Alzheimer's disease in brain MRI using machine learning architectures. An mRMR-based hybrid CNN was proposed in the study. First, features of MRI in Darknet53, InceptionV3, and Resnet101 models were extracted. These extracted features were concatenated. Then the obtained features were optimized using the mRMR method. SVM and KNN classifiers were used to classify the optimized features. The accuracy value obtained in the proposed model was 99.1%.

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基于mRMR的混合卷积神经网络模型在脑磁共振图像上对阿尔茨海默病进行分类

阿尔茨海默病是一种进行性神经退行性致命疾病，其特征是精神功能下降。尽管该病没有明确的治疗方法，但有一些治疗方法可以在早期诊断的情况下延缓病程。因此，疾病的早期诊断和分类对于确定最合适的治疗非常重要。以高软组织分辨率对大脑进行成像的最常用方法是磁共振成像 (MRI)。脑部 MRI 通过一些特定的影像学发现有助于诊断阿尔茨海默病。在这项研究中，我们旨在使用机器学习架构在脑部 MRI 中对阿尔茨海默病进行分类。该研究提出了一种基于mRMR的混合CNN。一、Darknet53、InceptionV3中MRI的特点，并提取了 Resnet101 模型。这些提取的特征被连接起来。然后使用mRMR方法优化获得的特征。SVM 和 KNN 分类器用于对优化后的特征进行分类。在所提出的模型中获得的准确度值为 99.1%。