Deep MRI segmentation: A convolutional method applied to alzheimer disease detection

30Citations
Citations of this article
46Readers
Mendeley users who have this article in their library.

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.

References Powered by Scopus

U-net: Convolutional networks for biomedical image segmentation

65940Citations
N/AReaders
Get full text

Going deeper with convolutions

39860Citations
N/AReaders
Get full text

Fully convolutional networks for semantic segmentation

24854Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Toward deep MRI segmentation for Alzheimer’s disease detection

47Citations
N/AReaders
Get full text

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group

42Citations
N/AReaders
Get full text

Deep-Learning-Based Diagnosis and Prognosis of Alzheimer's Disease: A Comprehensive Review

41Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Allioui, H., Sadgal, M., & Elfazziki, A. (2019). Deep MRI segmentation: A convolutional method applied to alzheimer disease detection. International Journal of Advanced Computer Science and Applications, 10(11), 365–371. https://doi.org/10.14569/IJACSA.2019.0101151

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 17

85%

Lecturer / Post doc 2

10%

Researcher 1

5%

Readers' Discipline

Tooltip

Computer Science 12

63%

Engineering 4

21%

Medicine and Dentistry 2

11%

Neuroscience 1

5%

Save time finding and organizing research with Mendeley

Sign up for free