Alzheimer's disease (AD) is a pressing global issue, demanding effective diagnostic
approaches. This systematic review surveys the recent literature (2018 onwards) to …

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging,
offering unprecedented opportunities for the diagnosis and treatment of neurological …

Alzheimer's disease (AD) is one of the most prevalent types of dementia, which primarily
affects people over age 60. In clinical practice, it is a challenging task to identify AD in its …

Mass spectrometry-based proteomic technique has become indispensable in current
exploration of complex and dynamic biological processes. Instrument development has …

The early diagnosis of subjects with mild cognitive impairment (MCI) is an effective
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …

Deep learning (DL) algorithms based on brain MRI images have achieved great success in
the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that …

In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to
deal with Feature Selection (FS) problems. FS is an important data reduction step in data …

Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …

Feature selection (FS) plays a crucial role as a pre-processing tool in data mining, especially
for real-world applications in medical fields; it has been utilized exponentially and becomes …

Feature selection can effectively define the feature subset, remove redundant, irrelevant,
and noisy features. In order to adapt the feature selection problem, this paper adopts role …