Deep learning (DL) pipelines have evolved for over a decade now and are efficient at
solving many challenging problems of image and signal processing applications. Designing …

As an early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) is able to be
detected by analyzing the brain connectivity networks. For this reason, we devise a new …

Multi-modal medical imaging information has been widely used in computer-assisted
investigations and diagnoses. A typical example is that the combination of information from …

Alzheimer's disease (AD) is an irreversible brain disease that severely damages human
thinking and memory. Early diagnosis plays an important part in the prevention and …

Multi-modal fusion has become an important data analysis technology in Alzheimer's
disease (AD) diagnosis, which is committed to effectively extract and utilize complementary …

Multimodal neuroimage can provide complementary information about the dementia, but
small size of complete multimodal data limits the ability in representation learning. Moreover …

Accurate prediction of high-risk group who may convert to Alzheimer's disease (AD) patients
is critical for the future treatment of patients. Recently, logistic regression is used for the early …

The abnormal accumulation of amyloid β protein (Aβ) is one of the most important causes of
Alzheimer's disease (AD) and is usually a detecting biomarker. Curcumin and its derivatives …

Purpose This paper is a systematic literature review of the use of artificial intelligence
techniques to detect early dementia. It focuses on multi-modal feature analysis in …

Over the past decade, Deep Learning (DL) pipelines have undergone significant evolution
and demonstrated effectiveness in addressing complex challenges within artificial …