IJCAIT Vol 13, Issue 2, 2022

Recognition, Analysis and classification of Alzheimer ailment using Hybrid Genetic and Particle Swarm with Deep Learning Technique
Authors: Manpreet Kaur, Rachhpal Singh, India.

Nature-inspired methods play significant role in different applications in arena of computer science, industrialized designs, business and medical field. Generally, these techniques are nature inspired that is helpful in segmentation of brain’s internal parts and related disease. Dementia is brain related Alzheimer’s disease (AD) that is very difficult for clinical identification, classification and supervision. Presently, AD has no remedy, but at initial stage there is an effective treatment. It is very important and challenging job to diagnosis of AD. Only it can be treated efficiently at early stages with the early prediction through various biomarkers. Deep Learning (DL) is the only best approach in the medical field for prediction. Depending upon the different hyper-parameters, the performance and convergence of Convolutional adopted Neural Networks (CNN) affect the prediction liable upon the input data. Number of nature inspired optimization approaches is developed for optimization and segmentation of the data. Particle Swarm Optimization process (PSO) and Genetic Algorithm (GA) are the best optimization algorithms for the diagnosis of AD. GA optimizes the hyper-parameters and configuration related with network architecture of this system. The proposed hybrid approach using GA and PSO with deep neural network classify and diagnose the disease efficiently after inspecting the brain’s magnetic resonance imaging (MRI). As classification error decreases with the increase of features that mend the accuracy of classification. Experimental outputs and comparisons show the accuracy of proposed approaches is comparatively better than other classification techniques.

Keywords: Alzheimer disease, Deep learning, Optimization technique, Genetic Algorithm, Particle Swarm Optimization, Convolutional Neural Networks, Magnetic Resonance Imaging.

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