… To improve diagnostic performance in AD/MCI identification, we further optimize the deep
network in a supervised manner. Accordingly, we stack another output layer on top of the SAE. …

… of their method for AD/MCI diagnosis, it is well-known that … deep learning based high-level
structural and functional feature representation from MRI and PET, respectively, for AD/MCI …

… from deep hidden layers of data. This study will design a deep learning-based framework
including a Gated Recurrent Unit (GRU) model for effective detection of MCI participants from …

… of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a
deep learning approach, which is one of the most powerful branches of machine learning, to …

… for the early diagnosis of AD and MCI based on deep learning. Compared to the conventional
binary classification methods, such as SVM, our method conducts AD diagnosis as a multi-…

… Inspired by independent component analysis (ICA), a widely used data-driven BFN modeling
method [12, 13], we propose a novel BFN-based deep learning framework that directly …

… , a new machine learning area, provide … deep learning techniques in AD diagnosis via
neuroimaging data. We specifically focused on the early diagnosis of AD, which could detect MCI …

… We did not include late MCI data but only eMCI in ADNI2, because the BFNs from the eMCIs
… , therefore, eMCI diagnosis is a more challenging tasE where deep learning may show its …

… In this paper, we developed a robust deep learning framework for AD diagnosis by fusing …
for the diagnosis. The selected features were subsequently processed by the deep learning …

… From the 16 papers included in this review, Table 2 provides the top results of diagnostic
classification and/or prediction of MCI to AD conversion. We compared only binary …