Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data.

The quantity and quality of a dataset play a crucial role in the performance of prediction models. Increasing the amount of data increases the computational requirements and can introduce negligible variations, outliers, and noise. These significantly impact the model performance. Thus, instance selection techniques are crucial for building prediction models with informative data, reducing the dataset size, improving performance, and minimizing computational costs. This study proposed a novel methodology for identifying the most informative two-dimensional slices derived from magnetic resonance imaging (MRI) to study Alzheimer's disease. The efficacy of our methodology was attributable to a hippocampus-centered analysis using data from multiple atlases. The methodology was evaluated by constructing convolutional neural networks to identify Alzheimer's disease, using a consolidated dataset constructed from three standard datasets: Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing, and Open Access Series of Imaging Studies. The proposed methodology demonstrated a commendable subject-level classification accuracy of approximately when distinguishing between normal cognition and Alzheimer's.