A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. (15th April 2021) - Mehmood, Atif; Yang, Shuyuan; Feng, Zhixi; Wang, Min; Ahmad, AL Smadi; Khan, Rizwan; Maqsood, Muazzam; Yaqub, Muhammad
Highlights: We developed a layer-wise transfer learning model for Alzheimer's Disease (AD) classification. We predict the best results on binary class classification such as NC, EMCI, LMCI and AD. To overcome the less training data issue and check the robustness of transfer learning and avoid overfitting. This study is based on gray matter (GM) scans that used for early diagnosis of AD. Abstract: Mild cognitive impairment (MCI) detection using magnetic resonance image (MRI), plays a crucial role in the treatment of dementia disease at an early stage. Deep learning architecture produces impressive results in such research. Algorithms require a large number of annotated datasets for training the model. In this study, we overcome this issue by using layer-wise transfer learning as well as tissue segmentation of brain images to diagnose the early stage of Alzheimer's disease (AD). In layer-wise transfer learning, we used the VGG architecture family with pre-trained weights. The proposed model segregates between normal control (NC), the early mild cognitive impairment (EMCI), the late mild cognitive impairment (LMCI), and the AD. In this paper, 85 NC patients, 70 EMCI, 70 LMCI, and 75 AD patients access form the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Tissue segmentation was applied on each subject to extract the gray matter (GM) tissue. In order to check the validity, the proposed method is tested on preprocessing data and achieved the highest rates of the classification accuracy on AD vs NC is 98.73%, also distinguish between EMCI vs LMCI patients testing accuracy 83.72%, whereas remaining classes accuracy is more than 80%. Finally, we provide a comparative analysis with other studies which shows that the proposed model outperformed the state-of-the-art models in terms of testing accuracy.