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.
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