Neurodegenerative Disease Alzheimer's and Parkinson's Classification with Deep Learning

Computer-aided devices and systems have become increasingly common in healthcare. These devices and systems contribute significantly to the faster and earlier diagnosis of diseases. Imaging devices, especially Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), play a major role in diseases where early diagnosis is crucial. In neurological disorders, the use of MRI and CT images as input data in deep learning models is becoming increasingly widespread. In this study, MRI images from the "Alzheimer Parkinson 3 Class Data Set" available on the Kaggle platform were used for the diagnosis of Alzheimer's and Parkinson's diseases. The dataset includes three classes: 2,561 Alzheimer's, 906 Parkinson's, and 3,010 Control (Normal) images. In this work, the Alzheimer, Parkinson, and Normal classes were trained using ResNet-18, VGG-16, and ConvNext architectures, yielding accuracy rates of 96.2%, 95.4%, and 98.9%, respectively. Additionally, Alzheimer and Parkinson diseases were tested against the normal class using binary classifiers. For the Alzheimer-Normal and Parkinson-Normal classes, the models achieved the following results: ResNet-18 with accuracy rates of 82.0% and 96.1%, VGG-16 with 95.4% and 89.4%, and ConvNext with 99.4% and 99.5%, respectively.