Mindmapper: Predicting Alzheimer’s With Magnetic Resonance Imaging

Abstract

Alzheimer’s, a progressive ailment impacting memory and overall brain function, lacks a definitive diagnostic test. More than just relying on brain scans is required for conclusive identification. Physicians assess Alzheimer's based on patient relatives' reports, social behaviour, and past medical records. Integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms could enhance this diagnostic approach. Big processing, harnessing data from diverse sources amidst evolving scenarios, offers a comprehensive perspective. This proposed solution involves a big processing model from a data mining standpoint. The paper employs various ML classifiers to train Alzheimer's detection models, treating attributes as a complex system. Notably, the Support Vector Machine (SVM) with a linear kernel model demonstrated superior accuracy compared. In the area of medical research, the prediction of Alzheimer’s disease using deep learning is of tremendous interest. It has been shown that deep learning models, particularly convolutional neural networks (CNNs), are highly accurate at identifying Alzheimer’s disease from MRI images. According to studies, CNNs can recognize complex features from MRI images and train to distinguish between people with and without Alzheimer’s disease. Large datasets of MRI scans are used to train the models, which are then used to boost their accuracy using several methods, such as data augmentation and transfer learning. Deep learning models for Alzheimer’s disease prediction have the potential to dramatically advance the illness’s early diagnosis and treatment, as well as support the creation of novel therapeutics.