Open Access

ARTICLE

Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

M. Vinoth Kumar^1,*, M. Prakash², M. Naresh Kumar³, H. Abdul Shabeer⁴

1 Department of Information Science and Engineering, Dayananda Sagar Academy of Technology & Management, Kanakapura Main Road, 560082, Bangalore, India
2 Department of Data Science and Business Systems School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, 603203, Tamilnadu, India
3 Department of ECE, Vardhaman College of Engineering, Hyderabad, 501218, India
4 IBM India Pvt Ltd., DLF IT Park, Chennai, 600125, India

* Corresponding Author: M. Vinoth Kumar. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1277-1293. https://doi.org/10.32604/iasc.2023.025591

Received 29 November 2021; Accepted 13 February 2022; Issue published 19 July 2022

Abstract

The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the level of disease severity, the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients rapidly. To avoid the problem of overfitting, a dropout has been added to LSTM. In the existing work, boundary detection of retinal layers was found to be inaccurate during the segmentation process of Optical Coherence Tomography (OCT) image and to overcome this issue; Particle Swarm Optimization (PSO) has been utilized. To the best of our understanding, this is the first paper to use Particle Swarm Optimization. When compared with the existing works, the proposed work is found to be performing better in terms of F1 Score, Precision, Recall, training loss, and segmentation accuracy and it is found that the prediction accuracy was increased to 10% higher than the existing systems.

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Keywords

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