Skip to main navigation menu Skip to main content Skip to site footer
Español (España) | English
Editorial
Revistas / Journals
Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home
Issue
Current Issue Previous Issues Thematic issues
About
About the Journal Editorial Team Privacy Statement Contact
Author Guidelines
Guidelines for authors Peer Review Process Open Access Policy and Copyright Digital preservation policy Ethical aspects Anti-plagiarism policy Crossmark policy page
Indexing
×
Español (España) | English
Editorial

Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home

Current Issue Previous Issues Thematic issues

About the Journal Editorial Team Privacy Statement Contact

Guidelines for authors Peer Review Process Open Access Policy and Copyright Digital preservation policy Ethical aspects Anti-plagiarism policy Crossmark policy page
Indexing
Original
Published: 2024-03-07
DOI: https://doi.org/10.56294/sctconf2024651

Classifying alzheimer's disease from sMRI data using a hybrid deep learning approaches

By
Mathews Emmanuel ,
Mathews Emmanuel

Department of Computer Science, Sathyabhama Institute of Science and Technology, Chennai. India

Search this author on:

PubMed | Google Scholar
J. Jabez ,
J. Jabez

Department of Information Technology, Sathyabhama Institute of Science and Technology, Chennai. India

Search this author on:

PubMed | Google Scholar

Abstract

The chance of developing "Alzheimer's Disease (AD)" increases every 5 years after 65 years of age, making it a particularly common form of neurodegenerative disorder among the older population. The use of "Magnetic Resonance Imaging (MRI)" to diagnose AD has grown in popularity in recent years. A further benefit of MRI is that it provides excellent contrast and exquisite structural detail. As a result, some studies have used biological markers backed by "structural MRI (sMRI)" to separate AD populations, which indicate differences in brain tissue size and degradation of the nervous system. The lack of properly segmented regions and essential features by the existing models might affect classification accuracy for AD. The categorization of AD in this study is based on sMRI. In this research, the hybrid Deep-Learning Models "SegNet and ResNet (SegResNet)" have been proposed for segmentation, feature extraction, and to classify the AD. SegNet network is used to identify and segment specific brain areas. Edges and circles are the SegNet's first levels, whereas the deeper layers acquire more nuanced and useful features. SegNet's last deconvolution layer produces a wide range of segmented images linked to the 3 categorization labels "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)", and "AD" which the machine has earlier found out. To increase classification performance, the attributes of each segmented sMRI image serve as strong features of the labels. To enhance the feature information used for classification, a feature vector is built by combining the values of the pixel intensity of the segmented sMRI images. ResNet-101 classifiers are then used for characterizing vectors to identify the presence or absence of AD or MCI in each sMRI image. In terms of detection and classification accuracy, the proposed SegResNet Model is superior to the existing KNN, EFKNN, AANFIS, and ACS approaches

Keywords:

Alzheimer's Disease,
sMRI,
DeepLearning,
SegNet,
ResNet,

How to Cite

1.
Mathews E, Jabez J. Classifying alzheimer’s disease from sMRI data using a hybrid deep learning approaches. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Mar. 7 [cited 2024 Jun. 26];3:651. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/651

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

Vol. 3 (2024)
See full issue

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.