Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease-Reference-Cited by-同舟云学术

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Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

Published:2023 Issue:3 Volume:74 Page:6909-6932
ISSN:1546-2226
Container-title:Computers, Materials & Continua
language:en
Short-container-title:

Author:

Aparna Mudiyala,Srinivasa Rao Battula

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Modeling and Simulation,Biomaterials

Reference48 articles.

1. Enhancing magnetic resonance imaging-driven;Zhou;Alzheimer’s Research & Therapy,2021

2. Stacked autoencoders as new models for an accurate Alzheimer’s disease classification support using resting-state EEG and MRI measurements;Ferri;Clinical Neurophysiology,2021

3. Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification;Divya;Neural Computing and Applications,2021

4. Alzheimer’s disease neuroimaging initiative: 3D shearlet-based descriptors combined with deep features for the classification of Alzheimer’s disease based on MRI data;Alinsaif;Computers in Biology and Medicine,2021

5. Colorectal multi-class image classification using deep learning models;Raju;Bulletin of Electrical Engineering and Informatics,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Brain Tumor Detection based on Multiple Deep Learning Models for MRI Images;EAI Endorsed Transactions on Pervasive Health and Technology;2024-03-21

2. A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques;IEEE Access;2023

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