Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multitask learning strategy into the deep learning framework. We applied the proposed method to the ADNI dataset, and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods.

Image quality assessment is one of the challenging field of digital image processing system. It can be done subjectively or objectively. PSNR is the most popular and widely used objective image quality metric but it is not correlate well with the subjective assessment. Thus, there are a lot of objective image quality metrics (IQM) developed in the past few decades to replace PSNR. This paper provides a literature review of the current subjective and objective image quality measures. The purpose of this paper is to collect reported quality metrics and group them according to their strategies and techniques.