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A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned
The successful early diagnosis of brain tumors plays a major role in improving the treatment
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …
Alzheimer disease detection techniques and methods: a review
Brain pathological changes linked with Alzheimer's disease (AD) can be measured with
Neuroimaging. In the past few years, these measures are rapidly integrated into the …
Neuroimaging. In the past few years, these measures are rapidly integrated into the …
Deep learning based pipelines for Alzheimer's disease diagnosis: a comparative study and a novel deep-ensemble method
Background Alzheimer's disease is a chronic neurodegenerative disease that destroys brain
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of
Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be …
Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be …
Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree
In this paper we proposed a novel classification system to distinguish among elderly
subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal …
subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal …
Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates
The trade-off between computation time and predictive accuracy is important in the design
and implementation of clinical decision support systems. Machine learning techniques with …
and implementation of clinical decision support systems. Machine learning techniques with …
Ensembles of patch-based classifiers for diagnosis of Alzheimer diseases
There is ongoing research for the automatic diagnosis of Alzheimer's disease (AD) based on
traditional machine learning techniques, and deep learning-based approaches are …
traditional machine learning techniques, and deep learning-based approaches are …
[HTML][HTML] Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter …
In recent times, accurate and early diagnosis of Alzheimer's disease (AD) plays a vital role in
patient care and further treatment. Predicting AD from mild cognitive impairment (MCI) and …
patient care and further treatment. Predicting AD from mild cognitive impairment (MCI) and …
[HTML][HTML] Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning
Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI
Abstract Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and
Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in …
Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in …