Abstract: The learning techniques have a particular need especially for the detection of invisible brain diseases. Learning-based methods rely on MRI medical images to reconstruct a solution for detecting aberrant values or areas in the human brain. In this article, we present a method that automatically performs segmentation of the brain to detect brain damage and diagnose Alzheimer's disease (AD). In order to take advantages of the benefits of 3D and reduce complexity and computational costs, we present a 2.5D method for locating brain inflammation and detecting their classes. Our proposed system is evaluated on a set of public data. Preliminary results indicate the reliability and effectiveness of our Alzheimer's Disease Detection System and demonstrate that our method is beyond current knowledge of Alzheimer's disease diagnosis.

Hanane Allioui, Mohamed Sadgal and Aziz Elfazziki, “Deep MRI Segmentation: A Convolutional Method Applied to Alzheimer Disease Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101151

@article{Allioui2019,
title = {Deep MRI Segmentation: A Convolutional Method Applied to Alzheimer Disease Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101151},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101151},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {Hanane Allioui and Mohamed Sadgal and Aziz Elfazziki}
}