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Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction 被引量:1
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作者 M.Vinoth kumar M.Prakash +1 位作者 m.naresh kumar H.Abdul Shabeer 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1277-1293,共17页
The most noteworthy neurodegenerative disorder nationwide is appar-ently the Alzheimer's disease(AD)which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinica... The most noteworthy neurodegenerative disorder nationwide is appar-ently 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 sen-sitive method for evaluating the AD has to be developed yet.Due to the correla-tions 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 atfinding the severity of AD from ocular biomarkers.Tofind 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 seg-mentation process of Optical Coherence Tomography(OCT)image and to over-come this issue;Particle Swarm Optimization(PSO)has been utilized.To the best of our understanding,this is thefirst paper to use Particle Swarm Optimization.When compared with the existing works,the proposed work is found to be per-forming better in terms of F1 Score,Precision,Recall,training loss,and segmen-tation accuracy and it is found that the prediction accuracy was increased to 10%higher than the existing systems. 展开更多
关键词 Alzheimer's disease enhanced LSTM particle swarm optimization OCT image boundary detection
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