This report investigates whether Alzheimer’s MRI scans could be classified more accurately using deep learning if the biological sex was considered. The data used in the study were female and male Alzheimer’s Disease (AD) and Cognitive Normal (CN) MRI scans. The data was divided into a training and test set. Three convolutional neural networks, based on the same architecture, were trained on different data, one on the female training data (female model), one on the male training data (male model), and one on both the female and male training data (combined model). The female model was tested on the female test data, the male model on the male test data and the combined model on first the female and then the male test data. The female model ...
Alzheimer’s Disease (AD) is the most common form of dementia and according to the World Health Organ...
The number of people suffering from Alzheimer’s disease (AD) is expected to increase rapidly in the ...
Convolutional neural networks have enabled significant improvements in medical image-based diagnosis...
This report investigates whether Alzheimer’s MRI scans could be classified more accurately using dee...
Alzheimer’s disease is a neurodegenerative disease that affects approximately 6% of the global popul...
Effective computer diagnosis of Alzheimer’s disease could bring large benefitsto the millions of peo...
Computer-aided-diagnosis (CAD) emerged in the early 1950s and since then CAD has facilitated the dia...
The identification of Alzheimer’s disease through the application of various machine learning techni...
Objective: To investigate the impact of sex on cognition among cognitively normal older people witho...
Alzheimer’s Disease (AD) is a progressive brain disorder affecting thinking, memory and behavior. It...
In this thesis, differences in the brain structure, clinical and cognitive scores as a function of ...
Beyond detecting brain lesions or tumors, comparatively little success has been attained in identify...
AbstractBeyond detecting brain lesions or tumors, comparatively little success has been attained in ...
Alzheimer disease (AD) is characterized by wide heterogeneity in cognitive and behavioural syndromes...
Accurate diagnosis in the early stages is an important challenge for the prevention and effective tr...
Alzheimer’s Disease (AD) is the most common form of dementia and according to the World Health Organ...
The number of people suffering from Alzheimer’s disease (AD) is expected to increase rapidly in the ...
Convolutional neural networks have enabled significant improvements in medical image-based diagnosis...
This report investigates whether Alzheimer’s MRI scans could be classified more accurately using dee...
Alzheimer’s disease is a neurodegenerative disease that affects approximately 6% of the global popul...
Effective computer diagnosis of Alzheimer’s disease could bring large benefitsto the millions of peo...
Computer-aided-diagnosis (CAD) emerged in the early 1950s and since then CAD has facilitated the dia...
The identification of Alzheimer’s disease through the application of various machine learning techni...
Objective: To investigate the impact of sex on cognition among cognitively normal older people witho...
Alzheimer’s Disease (AD) is a progressive brain disorder affecting thinking, memory and behavior. It...
In this thesis, differences in the brain structure, clinical and cognitive scores as a function of ...
Beyond detecting brain lesions or tumors, comparatively little success has been attained in identify...
AbstractBeyond detecting brain lesions or tumors, comparatively little success has been attained in ...
Alzheimer disease (AD) is characterized by wide heterogeneity in cognitive and behavioural syndromes...
Accurate diagnosis in the early stages is an important challenge for the prevention and effective tr...
Alzheimer’s Disease (AD) is the most common form of dementia and according to the World Health Organ...
The number of people suffering from Alzheimer’s disease (AD) is expected to increase rapidly in the ...
Convolutional neural networks have enabled significant improvements in medical image-based diagnosis...