Comparative Analysis of Alzheimer's & Parkinson Disease Identification using Deep Learning Approach for Precise Diagnosis
Keywords:
Alzheimer, Parkinson, CNN, VGG16, VGG19, InceptionV3Abstract
Alzheimer's disease (AD) and Parkinson Disease is a terrible, severe, and irreversible affliction, yet it also has a positive worldwide impact on human life. It was the sixth most common cause of mortality in the United States and could not be prevented by immunization. The most difficult aspect of discovering new species. The identification of the proteins and genes causing AD/PD will help in understanding the illness's and developing preventative or therapeutic measures. They look into any potential interactions between genes or proteins and Alzheimer's disease and Parkinson Disease using useful techniques and knowledge. A Deep Learning technique for predicting protein connections in Alzheimer's disease and Parkinson Disease was developed using up-to-date data from all known AD /PD proteins and genes. We proposed the comparative analysis approach since MR brain scans are frequently utilized for Alzheimer's diagnosis and Parkinson Disease. The MRI data set's background noise was removed using multi-layer perceptual (MLP) filtering. In the suggested study, we employ CNN VGG 16,VGG19 and InceptionV3 for training, the CNN Algorithm for classifying, the Edge-based for segmenting, and histogram equalization for image improvement. The proposed approach in this work offers a classification accuracy of up to based on experimental data. ADNI-82.20% , OASIS-95.35%.
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