An intelligent Alzheimer's disease diagnosis method using unsupervised feature learning

Today, the diagnosis of Alzheimer's disease (AD) or mild cognitive impairment (MCI) has attracted the attention of researchers in this field owing to the increase in the occurrence of the diseases and the need for early diagnosis. Unfortunately, the nature of high dimension of neural data and few available samples led to the creation of a precise computer diagnostic system. Machine learning techniques, especially deep learning, have been considered as a useful tool in this field. Inspired by the concept of unsupervised feature learning that uses artificial intelligence to learn features from raw data, a two-stage method was presented for an intelligent diagnosis of Alzheimer's disease. At the first stage of learning, scattered filtering, an uncontrolled two-layer neural network was used to directly learn features from raw data. At the second stage, SoftMax regression was used to categorize health statuses based on the learned features. The proposed method was validated by the data sets of Alzheimer's Brain Images. The results showed that the proposed method achieved very good diagnostic accuracy and was better than the existing methods for brain image data sets. The proposed method reduces the need for human work and makes it easy to intelligently diagnose for big data processing, because the learning features are adaptive. In our experiments with the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, a dual and multi-class classification was conducted for AD/MCI diagnosis and the superiority of the proposed method in comparison with the advanced methods was shown.