Enhancing insights: unravelling the potential of preprocessing MRI for artificial intelligence based Alzheimer's disease classification

Alzheimer’s Disease (AD) is a common neurological disorder that causes gradual cognitive decline. Magnetic Resonance Imaging (MRI) is a helpful tool in diagnosing and categorizing AD. In the recent past, a lot of research has been performed in developing Artificial Intelligence based methods to automatically diagnose AD. Still, the effectiveness of the diagnosis performed by AI algorithms depends on the quality of the preprocessing applied to the input images. This chapter reviews various preprocessing techniques to improve MRI image quality and extract relevant features for AI based AD classification. The techniques include reorientation, registration, skull stripping, and slicing which are discussed in detail, along with their impact on image quality and classification performance. The chapter also addresses the challenges and potential pitfalls of preprocessing MRI images for AI based AD classification and explores emerging trends and advanced techniques. The importance of standardized preprocessing pipelines and the need for further research in optimizing preprocessing methods to enhance the accuracy and reliability of AI based AD classification is emphasized. The chapter also provides valuable insights into the preprocessing steps required to improve the suitability of MRI images for AI based AD classification, which can lead to early and accurate diagnosis of AD leading to the development of effective treatment strategies.