Predicting the Early Stages of the Alzheimer’s Disease via Combined Brain Multi-projections and Small Datasets

Kauê Duarte, Pedro V. de Paiva, Paulo Martins, Marco Carvalho

Abstract

Alzheimer is a neurodegenerative disease that usually affects the elderly. It compromises a patient’s memory, his/her cognition, and perception of the environment. Alzheimer’s Disease detection in its initial stage, known as Mild Cognitive Impairment, attracts special efforts from experts due to the possibility of using drugs to delay the progression of the disease. This paper aims to provide a method for the detection of this impairment condition via the classification of brain images using Transfer Learning - Deep Features and Support Vector Machine. The small number of images used in this work justifies the application of Transfer Learning, which employs weights from VGG19 initial layers used for ImageNet classification as deep features extractor, and then applies Support Vector Machines. Majority Voting, False-Positive Priori, and Super Learner were applied to combine previous classifiers predictions. The final step was a detection to assign a label to the previous voting outcomes, determining the presence or absence of an Alzheimers pre-condition. The OASIS-1 database was used with a total of 196 images (axial, coronal, and sagittal). Our method showed a promising performance in terms of accuracy, recall and specificity.

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Paper Citation


in Harvard Style

Duarte K., V. de Paiva P., Martins P. and Carvalho M. (2019). Predicting the Early Stages of the Alzheimer’s Disease via Combined Brain Multi-projections and Small Datasets.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 553-560. DOI: 10.5220/0007404705530560


in Bibtex Style

@conference{visapp19,
author={Kauê Duarte and Pedro V. de Paiva and Paulo Martins and Marco Carvalho},
title={Predicting the Early Stages of the Alzheimer’s Disease via Combined Brain Multi-projections and Small Datasets},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2019},
pages={553-560},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007404705530560},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Predicting the Early Stages of the Alzheimer’s Disease via Combined Brain Multi-projections and Small Datasets
SN - 978-989-758-354-4
AU - Duarte K.
AU - V. de Paiva P.
AU - Martins P.
AU - Carvalho M.
PY - 2019
SP - 553
EP - 560
DO - 10.5220/0007404705530560