Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection

Abstract

Alzheimer’s disease (AD) presents an acute global health challenge, with a sharp rise in cases prompting urgent action from the healthcare community. Recognizing the potential of Artificial Intelligence (AI) in early detection and treatment planning, researchers are focusing on optimizing MRI scans, a key diagnostic tool. However, MRI images often suffer from distortions like motion artifact and intensity fluctuations, compromising AI predictions. This study investigates MRI artifacts and proposes preprocessing solutions such as reorientation, registration, skull stripping, and slicing to enhance image quality. Integration into a user-friendly GUI aims to streamline healthcare operations, empowering medical professionals with accurate AD predictions. This research advances early detection efforts, improving patient outcomes and fostering innovation in medical imaging.