Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration

Juan A. Castro-Silva, Juan A. Castro-Silva, Maria Moreno-Garcia, Lorena Guachi-Guachi, Diego H. Peluffo-Ordoñez, Diego H. Peluffo-Ordoñez

2024

Abstract

Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instance selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.

Paper Citation

in Harvard Style

A. Castro-Silva J., Moreno-Garcia M., Guachi-Guachi L. and H. Peluffo-Ordoñez D. (2024). Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 453-460. DOI: 10.5220/0012469600003654

in Bibtex Style

@conference{icpram24,
author={Juan A. Castro-Silva and Maria Moreno-Garcia and Lorena Guachi-Guachi and Diego H. Peluffo-Ordoñez},
title={Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={453-460},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012469600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration
SN - 978-989-758-684-2
AU - A. Castro-Silva J.
AU - Moreno-Garcia M.
AU - Guachi-Guachi L.
AU - H. Peluffo-Ordoñez D.
PY - 2024
SP - 453
EP - 460
DO - 10.5220/0012469600003654
PB - SciTePress