Advancing Alzheimer's Disease Detection: Integrating Machine Learning And Image Analysis For Accurate Diagnosis
Keywords:
AD, CNN, Classification, EADD, MLP, RESNet150Abstract
Alzheimer's disease (AD) has a beneficial global impact on human life despite being a difficult, incurable, and horrible condition. Since it was not preventable by immunization, it was the sixth leading cause of death in the USA. The toughest part of finding new organisms. Understanding the cause of AD and finding ways to prevent or cure it will benefit from the discovery of proteins and genes involved in the illness. They employ practical tools and expertise to investigate the possible interaction between genes/proteins with Alzheimer's. Current information from all known AD proteins/genes was utilized to construct a machine-learning method for protein-connection prediction in Alzheimer's disease. Since MR brain scans are often used for diagnosing Alzheimer's, we suggested the EADD (Enhanced Alzheimer's Disease Detection) method. Multi-layer perceptual (MLP) was used to filter out the background noise in the MRI data set. In this proposed study, we use Histogram equalization to improve images, the Edge-based Robert operator to segment them, CNN with RESNet150 to train them, and the CNN Algorithm to classify them. Based on experimental data, the suggested method in this study has a classification accuracy of up to 98%.
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