A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

MK Abd-Ellah, AI Awad, AAM Khalaf… - Magnetic resonance …, 2019 - Elsevier
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 …

Alzheimer disease detection techniques and methods: a review

S Afzal, M Maqsood, U Khan, I Mehmood, H Nawaz… - 2021 - reunir.unir.net
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 …

Deep learning based pipelines for Alzheimer's disease diagnosis: a comparative study and a novel deep-ensemble method

A Loddo, S Buttau, C Di Ruberto - Computers in biology and medicine, 2022 - Elsevier
Background Alzheimer's disease is a chronic neurodegenerative disease that destroys brain
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

A Ortiz, J Munilla, JM Gorriz… - International journal of …, 2016 - World Scientific
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 …

Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree

YD Zhang, S Wang, Z Dong - Progress In Electromagnetics Research, 2014 - jpier.org
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 …

Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates

M Nilashi, H Ahmadi, AA Manaf, TA Rashid… - International Journal of …, 2020 - Springer
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 …

Ensembles of patch-based classifiers for diagnosis of Alzheimer diseases

S Ahmed, KY Choi, JJ Lee, BC Kim, GR Kwon… - IEEE …, 2019 - ieeexplore.ieee.org
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 …

[HTML][HTML] Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter …

S Basheera, MSS Ram - Alzheimer's & Dementia: Translational Research & …, 2019 - Elsevier
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 …

[HTML][HTML] Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning

AW Mulyadi, W Jung, K Oh, JS Yoon, KH Lee, HI Suk - NeuroImage, 2023 - Elsevier
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 …

A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI

S Basheera, MSS Ram - Computerized Medical Imaging and Graphics, 2020 - Elsevier
Abstract Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and
Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in …