An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke-Reference-Cited by-同舟云学术

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An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

Published:2024 Issue: Volume:12 Page:53189-53204
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sakri Sapiah¹^ORCID,Basheer Shakila¹^ORCID,Zain Zuhaira Muhammad¹^ORCID,Ismail Nurul Halimatul Asmak²^ORCID,Nassar Dua’ Abdellatef¹,Aldehim Ghadah Nasser¹^ORCID,Alharaki Mais Ayman¹^ORCID

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

2. Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Funder

Deputyship for Research and Innovation, Ministry of Education, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10494753.pdf?arnumber=10494753

Reference33 articles.

1. Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients

2. A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications

3. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

4. Deep Learning in Mining Biological Data

5. Predicting Ischemic Stroke Outcome Using Deep Learning Approaches

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