Applications of deep learning in Alzheimer's disease: a systematic literature review of current trends, methodologies, challenges, innovations, and future directions
S Toumaj, A Heidari, R Shahhosseini… - Artificial Intelligence …, 2024 - Springer
Alzheimer's Disease (AD) constitutes a significant global health issue. In the next 40 years, it
is expected to affect 106 million people. Although more and more people are getting AD …
is expected to affect 106 million people. Although more and more people are getting AD …
Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks
X Zhu, S Sun, L Lin, Y Wu, X Ma - Reviews in the Neurosciences, 2024 - degruyter.com
In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a
formidable neural network architecture, gaining significant traction in neuroimaging-based …
formidable neural network architecture, gaining significant traction in neuroimaging-based …
Early diagnosis of Alzheimer's disease using a group self-calibrated coordinate attention network based on multimodal MRI
X Yu, J Liu, Y Lu, S Funahashi, T Murai, J Wu, Q Li… - Scientific Reports, 2024 - nature.com
Convolutional neural networks (CNNs) for extracting structural information from structural
magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging …
magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging …
Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI
This study addresses the pervasive and debilitating impact of Alzheimer's disease (AD) on
individuals and society, emphasizing the crucial need for timely diagnosis. We present a …
individuals and society, emphasizing the crucial need for timely diagnosis. We present a …
Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging
VK Prasad, A Verma, P Bhattacharya, S Shah… - Scientific Reports, 2024 - nature.com
Abstract Recently, Deep Learning (DL) models have shown promising accuracy in analysis
of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic …
of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic …
Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation
State of Charge (SOC) estimation is vital for battery management systems (BMS), impacting
battery efficiency and lifespan. Accurate SOC estimation is challenging due to battery …
battery efficiency and lifespan. Accurate SOC estimation is challenging due to battery …
Alzheimer's disease classification based on brain region-to-sample graph convolutional network
Alzheimer's disease (AD) is a notable high prevalence neurodegenerative disorder
worldwide. Graph convolutional network (GCN) have emerged as a prominent technique for …
worldwide. Graph convolutional network (GCN) have emerged as a prominent technique for …
Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review
Abstract Background and Aims Alzheimer's disease (AD) is a degenerative neurological
condition that worsens over time and leads to deterioration in cognitive abilities, reduced …
condition that worsens over time and leads to deterioration in cognitive abilities, reduced …
深度学习在轻度认知障碍分类诊断中的应用.
周启香， 王晓燕， 张文凯， 贺鑫 - Journal of Frontiers of …, 2024 - search.ebscohost.com
阿尔兹海默症是一种不可逆的神经退行性疾病, 至今尚无彻底治愈可能, 但可通过早期干预延缓
其进展. 轻度认知障碍是阿尔兹海默症的初始阶段, 正确识别该阶段对阿尔兹海默症早期诊断 …
其进展. 轻度认知障碍是阿尔兹海默症的初始阶段, 正确识别该阶段对阿尔兹海默症早期诊断 …
Research on Human-Machine Collaborative Aesthetic Decision-Making and Evaluation Methods in Automotive Body Design: Based on DCGAN and ANN Models
S Zeng, Y Cai, R Zhang, X Lyu - IEEE Access, 2024 - ieeexplore.ieee.org
The main content of this study is the human-machine collaborative design research, taking
the car body design as the carrier. The research framework focused on two phases of car …
the car body design as the carrier. The research framework focused on two phases of car …