Alzheimers Disease Classification with Deep Learning Approaches

Early and appropriate diagnosis of Alzheimer's disease is critical in the onset of proper medical treatment and intervention. The current study presents a deep learning approach using the DINOv2 and ConvNeXt models for the early detection of cognitive impairment based on neuroimaging data. DINOv2 employs self-supervised learning to obtain the useful features of the unlabeled MRIs and thus to tackle the problem of limited annotated data. These are pre-trained on heterogeneous AD classification data. ConvNeXt is a novel convolutional neural network that improves the capacity of the model to classify and extract features, and diagnose faster and more accurately. The process involves MRI data preprocessing via normalization, resizing, and augmentation, as well as training and testing the model. The measures of performance are accuracy, precision, recall, and specificity. Initial results show superior efficacy in discriminating patients from control subjects at different stages of AD. The study holds potential in leveraging self-supervised and convolutional models to create diagnostic competence and allow early intervention. The system under proposal holds promise in revolutionizing the process of diagnosing neuro degenerative conditions such as Alzheimer’s Diseases.