|
|
|
AUTHOR(S): Sarah A. Soliman, Heba Mohsen, El-Sayed A. El-Dahshan, Abdel-Badeeh M. Salem
|
|
TITLE Exploiting of Machine Learning Paradigms in Alzheimer's Disease |
 PDF |
|
ABSTRACT The importance of using machine learning techniques in the field of medical imaging technologies have increased in both research and clinical care over the recent years. So, detecting Alzheimer's disease in precise way and early phase is essential for patient care. Researchers have been dedicating their efforts to evaluate compulsive changes that happen in the brain through the process of Alzheimer’s disease by neuroimaging techniques. Neuroimaging is becoming a progressively beneficial tool in understanding the pathogenesis of AD progress. Moreover neuroimaging playing an essential role in detecting AD using machine learning paradigms. Several machine learning techniques like Support Vector Machines, Artificial Neural Network, Deep Learning, K-Nearest Neighbour, K-means and Naive Bayes have been proposed to classify AD and to build CAD systems capable of detecting Alzheimer's at any stage. This paper is aim to present the medical aspects of AD, give review of AD biomarkers especially neuroimaging biomarkers and its techniques and finally presents a state-of-the-art review of the researches achieved on AD diagnosis. |
|
KEYWORDS Alzheimer's disease, Biomarker, Neuroimaging technologies, deep learning, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks (NNs) |
|
|
|
Cite this paper Sarah A. Soliman, Heba Mohsen, El-Sayed A. El-Dahshan, Abdel-Badeeh M. Salem. (2019) Exploiting of Machine Learning Paradigms in Alzheimer's Disease. International Journal of Psychiatry and Psychotherapy, 4, 1-11 |
|
|
