Efficient Detection of Alzheimer's Disease on Early Stages Using Deep Learning on MRI Data

Alzheimer's disease (AD) is a human brain disorder mainly affecting the older persons, which could be leads to cognitive decline and memory loss. The detection of Alzheimer's considered the important because of its slow disease progression and checking high quality of the human brain image for patients. This main aim of this work is detecting the human brain disease using deep learning techniques on Magnetic Resonance Imaging (MRI) data for the processing the brain and display the results is whether the person is affected the disease or normal condition in the brain. The MRI scans detailed information on the brain structures is used to spot the subtle changes that can be occur, once a person receives an onset of Alzheimer's Disease. This work involves preprocessing of MRI data to process the image quality and remove noise, followed by feature extraction to capture critical patterns which are main indicative of the disease. The Convolutional Neural Networks (CNNs) are applied to the MRI images for analyzing to use their ability to automatically detect the complex patterns in high-dimensional data. Various architectures are trained and evaluated for performance metrics such as accuracy, sensitivity, and specificity to classify most effective model in order to detect the Alzheimer's. The comparative outcomes establish the possible of deep learning to outperform traditional diagnostic methods by offering faster, more accurate, and reliable predictions. This work is used to the field of medical imaging and significant suggestions for improving Alzheimer's diagnosis and advancing accurate medicine.