Early diagnosis of Alzheimer's disease based on deep learning: A systematic review

Background: The improvement of health indicators and life expectancy, especially in developed countries, has led to population growth and increased age-related diseases, including Alzheimer's disease (AD). Thus, the early detection of AD is valuable to stop its progress at an early stage. Method: This study systematically reviewed the current state of using deep learning methods on neuroimaging data for timely diagnose of AD. We reviewed different deep models, modalities, feature extraction strategies, and parameter initialization methods to find out which model or strategy could offer better performance. Results: Our search in eight different databases resulted in 736 studies, from which 74 studies were included to be reviewed for data analysis. Most studies have reported the normal control (NC)/AD classification and have shown desirable results. Although recent studies showed promising results of utilizing deep models on the NC/mild cognitive impairment (MCI) and NC/early MCI (eMCI), other classification groups should be taken into consideration and improved. Discussion: The results of our review indicate that the comparative analysis is challenging in this area due to the lack of a benchmark platform; however, convolutional neural network (CNN)-based models, especially in an ensemble way, seem to perform better than other deep models. The transfer learning approach also could efficiently improve the performance and time complexity. Further research on designing a benchmark platform to facilitate the comparative analysis is recommended.