Alzheimer Disease Progression Forecasting: Empowering Models Through hybrid of CNN and LSTM with PSO Op-Timization-Reference-Cited by-同舟云学术

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Alzheimer Disease Progression Forecasting: Empowering Models Through hybrid of CNN and LSTM with PSO Op-Timization

Published:2024-03-05 Issue: Volume:6 Page:1-5
ISSN:
Container-title:2024 International Conference on Emerging Smart Computing and Informatics (ESCI)
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Author:

Deshpande Pallavi¹,Dhabliya Ritika²,Khubalkar Deepti³,Upadhye Pawan Arunkumar⁴,Wagh Kapil Adhar⁵,Khetani Vinit⁶

Affiliation:

1. EnTC, VIIT,Pune

2. Yashika Journal Publications Pvt. Limited,Wardha,Maharashtra,India

3. Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University),Pune,India

4. Bharati Vidyapeeth College Of engineering,Department of Electronics & Telecommunication,Pune,Maharashtra,India

5. Nutan Maharashtra Institute of Engineering & Technology, Talegaon(D),Pune,India(MS)

6. Cybrix Technologies,Nagpur,Maharashtra,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10497186/10497189/10497309.pdf?arnumber=10497309

Reference21 articles.

1. A review of the application of deep learning in the detection of Alzheimer's disease

2. Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimers disease: A machine learning approach;Aditya,2017

3. A systematic review on early prediction of Mild cognitive impairment to alzheimers using machine learning algorithms

4. An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images

5. Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer’s Disease from Mild Cognitive Impairment

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