Open AccessAn Analysis of Deep Learning Techniques in Neuroimaging
Author(s) -
C Narmatha,
AUTHOR_ID,
Hayam Alatawi,
Hibah Qasem Alatawi,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
journal of computational science and intelligent technologies
Language(s) - English
Resource type - Journals
ISSN - 2582-9041
DOI - 10.53409/mnaa/jcsit/2102
Subject(s) - deep learning , artificial intelligence , neuroimaging , convolutional neural network , computer science , dementia , machine learning , domain (mathematical analysis) , disease , psychology , neuroscience , medicine , pathology , mathematics , mathematical analysis
Deep learning is a machine learning technique that has demonstrated better results and performance when compared to standard machine learning algorithms in relation to higher dimensional MRI brain imaging data. The applications of deep learning in the clinical domain are discussed in this study. A detailed analysis of several deep learning algorithms for the Alzheimer's disease diagnosis is analyzed, in which this disorder of brain that gradually spreads and destroys memory of the brain, and it is a typical disorder in elderly individuals due to dementia. When it comes to brain image processing, the most commonly used and represented method, according to most research publications, is Convolutional Neural Networks (CNN). Following a review of many relevant studies for the Alzheimer's disease diagnosis, it was shown that utilizing advanced deep learning algorithms in different datasets (OASIS and ADNI) combined to one can improve AD prediction at earlier stages.