Machine learning techniques for the diagnosis of Alzheimer's disease: A review
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …
elderly population. Efficient automated techniques are needed for early diagnosis of …
Deep learning for Alzheimer's disease diagnosis: A survey
M Khojaste-Sarakhsi, SS Haghighi… - Artificial intelligence in …, 2022 - Elsevier
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images
S Murugan, C Venkatesan, MG Sumithra, XZ Gao… - Ieee …, 2021 - ieeexplore.ieee.org
Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily
worsens from mild to severe, impairing one's ability to complete any work without assistance …
worsens from mild to severe, impairing one's ability to complete any work without assistance …
A transfer learning approach for early diagnosis of Alzheimer's disease on MRI images
Mild cognitive impairment (MCI) detection using magnetic resonance image (MRI), plays a
crucial role in the treatment of dementia disease at an early stage. Deep learning …
crucial role in the treatment of dementia disease at an early stage. Deep learning …
A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
Electroencephalographic (EEG) recordings generate an electrical map of the human brain
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
A hybrid deep neural network for classification of schizophrenia using EEG Data
J Sun, R Cao, M Zhou, W Hussain, B Wang, J Xue… - Scientific Reports, 2021 - nature.com
Schizophrenia is a serious mental illness that causes great harm to patients, so timely and
accurate detection is essential. This study aimed to identify a better feature to represent …
accurate detection is essential. This study aimed to identify a better feature to represent …
A two-stage intrusion detection system with auto-encoder and LSTMs
Abstract 'Curse of dimensionality'and the trade-off between low false alarm rate and high
detection rate are the major concerns while designing an efficient intrusion detection system …
detection rate are the major concerns while designing an efficient intrusion detection system …
A novel statistical analysis and autoencoder driven intelligent intrusion detection approach
In the current digital era, one of the most critical and challenging issues is ensuring
cybersecurity in information technology (IT) infrastructures. With significant improvements in …
cybersecurity in information technology (IT) infrastructures. With significant improvements in …
Efficient deep neural networks for classification of Alzheimer's disease and mild cognitive impairment from scalp EEG recordings
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 …
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
RETRACTED ARTICLE: EEG signal classification using LSTM and improved neural network algorithms
Neural network (NN) finds role in variety of applications due to combined effect of feature
extraction and classification availability in deep learning algorithms. In this paper, we have …
extraction and classification availability in deep learning algorithms. In this paper, we have …