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Authors: Kauê T. N. Duarte ; Pedro V. V. de Paiva ; Paulo S. Martins and Marco A. G. Carvalho

Affiliation: School of Technology, University of Campinas (UNICAMP), R. Paschoal Marmo, Limeira and Brazil

Keyword(s): Classification, Transfer Learning, Mild Cognitive Impairment, Clinical Dementia Rating, Support Vector Machines.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Medical Image Applications

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 outcome s, 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. (More)

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Paper citation in several formats:
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 (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 553-560. DOI: 10.5220/0007404705530560

@conference{visapp19,
author={Kauê T. N. Duarte. and Pedro V. {V. de Paiva}. and Paulo S. Martins. and Marco A. G. 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 (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={553-560},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007404705530560},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - 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
IS - 2184-4321
AU - Duarte, K.
AU - V. de Paiva, P.
AU - Martins, P.
AU - Carvalho, M.
PY - 2019
SP - 553
EP - 560
DO - 10.5220/0007404705530560
PB - SciTePress