Authors:
Juan A. Castro-Silva
1
;
2
;
Maria Moreno-Garcia
1
;
Lorena Guachi-Guachi
3
and
Diego H. Peluffo-Ordoñez
4
;
5
Affiliations:
1
Universidad de Salamanca, Salamanca, Spain
;
2
Universidad Surcolombiana, Neiva, Colombia
;
3
Department of Mechatronics, International University of Ecuador, Simon Bolivar Avenue 170411, Quito, Ecuador
;
4
College of Computing, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid Ben Guerir, 43150, Morocco
;
5
SDAS Research Group
Keyword(s):
Alzheimer’s Disease, Swin Transformer, Weighted Ensemble, Instance Selection, Multipĺe Region of Interest.
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 instanc
e 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.
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