Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review

Z Zhao, JH Chuah, KW Lai, CO Chow… - Frontiers in …, 2023 - frontiersin.org
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation
and cognitive function impairment in elderly people. The irreversible and devastating …

Automatic detection of Alzheimer's disease using deep learning models and neuro-imaging: current trends and future perspectives

T Illakiya, R Karthik - Neuroinformatics, 2023 - Springer
Deep learning algorithms have a huge influence on tackling research issues in the field of
medical image processing. It acts as a vital aid for the radiologists in producing accurate …

[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals

SK Khare, UR Acharya - Knowledge-Based Systems, 2023 - Elsevier
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …

A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images

M Ashtari-Majlan, A Seifi… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild
cognitive impairment (MCI), is critical since some patients with progressive MCI will develop …

MobileNetV1-based deep learning model for accurate brain tumor classification

MM Mijwil, R Doshi, KK Hiran… - Mesopotamian …, 2023 - journals.mesopotamian.press
Brain tumors are among the most dangerous diseases that lead to mortality after a period of
time from injury. Therefore, physicians and healthcare professionals are advised to make an …

Automatic detection of Alzheimer's disease from EEG signals using low-complexity orthogonal wavelet filter banks

DV Puri, SL Nalbalwar, AB Nandgaonkar… - … Signal Processing and …, 2023 - Elsevier
Background: Alzheimer's disease (AD) is one of the most common neurodegenerative
disorder. As the incidence of AD is rapidly increasing worldwide, detecting it at an early …

Artificial intelligence for cognitive health assessment: state-of-the-art, open challenges and future directions

AR Javed, A Saadia, H Mughal, TR Gadekallu… - Cognitive …, 2023 - Springer
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led
many researchers to explore ways to automate the process to make it more objective and to …

Alzheimer's disease diagnosis using machine learning: a survey

OA Dara, JM Lopez-Guede, HI Raheem, J Rahebi… - Applied Sciences, 2023 - mdpi.com
Alzheimer's is a neurodegenerative disorder affecting the central nervous system and
cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition …

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

E Kaplan, M Baygin, PD Barua, S Dogan… - Medical Engineering & …, 2023 - Elsevier
Purpose The classification of medical images is an important priority for clinical research
and helps to improve the diagnosis of various disorders. This work aims to classify the …

Automated prediction system for Alzheimer detection based on deep residual autoencoder and support vector machine

M Menagadevi, S Mangai, N Madian, D Thiyagarajan - Optik, 2023 - Elsevier
Alzheimer's disease (AD) is a type of neurological disorder and is a most frequent cause of
dementia across the world. The area of medical imaging has created an advancement in …