A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images - Khan, Rizwan; Akbar, Saeed; Mehmood, Atif; Shahid, Farah; Munir, Khushboo; Ilyas, Naveed; Asif, M.; Zheng, Zhonglong (Frontiers in Neuroscience, 2023)
Alzheimer's is an acute degenerative disease affecting the elderly population in all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this work, we propose a transfer learning base approach to classify various stages of AD The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In this regard, we applied tissue segmentation to extract the grey matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this grey matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with stepwise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning the new features efficiently. Extensive experiments are conducted to evaluate the perform ace of the proposed architecture. The overall comparison demonstrates that the proposed method outperformed the existing methods in terms of objective evaluations.