A new deep belief network-based multi-task learning for diagnosis of Alzheimer's disease

Accurate classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI), especially distinguishing the progressive MCI (pMCI) from stable MCI (sMCI), will be helpful in both reducing the risk of converting into AD and also releasing the burden on the family and even the society. In this study, a novel deep belief network (DBN) based multi-task learning algorithm is developed for the classification issue. In particular, the dropout technology and zero-masking strategy are exploited for getting over the overfitting problem and also enhancing the generalization ability and robustness of the model. Then, a new framework based on the DBN-based multi-task learning is established for accurate diagnosis of AD. After MRI preprocessing, not only the principal component analysis is utilized to reduce the feature dimension, but also multi-task feature selection approach is introduced to select the feature set related to all tasks as a result of taking the internal relevancy among multiple related tasks into consideration. Using data from the ADNI dataset, our method achieves satisfactory results in six tasks of health control (HC) vs. AD, HC vs. pMCI, HC vs. sMCI, pMCI vs. AD, sMCI vs. AD and sMCI vs. pMCI with the accuracies are 98.62%, 96.67%, 92.31%, 91.89%, 99.62% and 87.78%, respectively. Experimental results demonstrate that the DBN-based MTL algorithm developed in this study is an effective, superior and practical method of AD diagnosis.

Automated summary

This study develops a novel deep belief network (DBN) based multi-task learning algorithm for accurate classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI), with a focus on distinguishing progressive MCI (pMCI) from stable MCI (sMCI). The algorithm achieves satisfactory results in six different classification tasks using data from the ADNI dataset, demonstrating its effectiveness and practicality.