Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI

J. Castro-Silva, J. Castro-Silva, J. Castro-Silva, M. Moreno-García, Lorena Guachi-Guachi, Lorena Guachi-Guachi, D. Peluffo-Ordóñez, D. Peluffo-Ordóñez

2022

Abstract

The selection of more informative instances from a dataset is an important preprocessing step that can be applied in many classification tasks. Since databases are becoming increasingly large, instance selection techniques have been used to reduce the data to a manageable size. Besides, the use of test data in any part of the training process, called data leakage, can produce a biased evaluation of classification algorithms. In this context, this work introduces an instance selection methodology to avoid data leakage using an early subject, volume, and slice dataset split, and a novel percentile-position-analysis method to identify the regions with the most informative instances. The proposed methodology includes four stages. First, 3D magnetic resonance images are prepared to extract 2D slices of all subjects and only one volume per subject. Second, the extracted 2D slices are evaluated in a percentile distribution fashion in order to select the most insightful 2D instances. Third, image preprocessing techniques are used to suppress noisy data, preserving semantic information in the image. Finally, the selected instances are used to generate the training, validation and test datasets. Preliminary tests are carried out referring to the OASIS-3 dataset to demonstrate the impact of the number of slices per subject, the preprocessing techniques, and the instance selection method on the overall performance of CNN-based classification models such as DenseNet121 and EfficientNetB0. The proposed methodology achieved a competitive overall accuracy at a slice level of about 77.01% in comparison to 76.94% reported by benchmark- and-recent works conducting experiments on the same dataset and focusing on instance selection approaches.

Paper Citation

in Harvard Style

Castro-Silva J., Moreno-García M., Guachi-Guachi L. and Peluffo-Ordóñez D. (2022). Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 330-337. DOI: 10.5220/0010900100003122

in Bibtex Style

@conference{icpram22,
author={J. Castro-Silva and M. Moreno-García and Lorena Guachi-Guachi and D. Peluffo-Ordóñez},
title={Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={330-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010900100003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI
SN - 978-989-758-549-4
AU - Castro-Silva J.
AU - Moreno-García M.
AU - Guachi-Guachi L.
AU - Peluffo-Ordóñez D.
PY - 2022
SP - 330
EP - 337
DO - 10.5220/0010900100003122