Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images

Abstract Alzheimer’s disease, the most common form of dementia is a neurodegenerative brain order that has currently no cure for it. Hence, early diagnosis of such disease using computer-aided systems is a subject of great importance and extensive research amongst researchers. Nowadays, deep learning or particularly convolutional neural network (CNN) is getting more attention due to its state-of-the-art performances in variety of computer vision tasks such as visual object classification, detection and segmentation. Several recent studies, that have used brain MRI scans and deep learning have shown promising results for diagnosis of Alzheimer’s disease. However, most common issue with deep learning architectures such as CNN is that they require large amount of data for training. In this paper, a mathematical model P F S E C TL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task. Experimentation is performed on data collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The accuracy of the 3-way classification using the described method is 95.73% for the validation set.