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Proceedings Paper

Automatic pathology classification using a single feature machine learning support - vector machines
Author(s): Fernando Yepes-Calderon; Fabian Pedregosa; Bertrand Thirion; Yalin Wang; Natasha Lepore
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Paper Abstract

Magnetic Resonance Imaging (MRI) has been gaining popularity in the clinic in recent years as a safe in-vivo imaging technique. As a result, large troves of data are being gathered and stored daily that may be used as clinical training sets in hospitals. While numerous machine learning (ML) algorithms have been implemented for Alzheimer’s disease classification, their outputs are usually difficult to interpret in the clinical setting. Here, we propose a simple method of rapid diagnostic classification for the clinic using Support Vector Machines (SVM)1 and easy to obtain geometrical measurements that, together with a cortical and sub-cortical brain parcellation, create a robust framework capable of automatic diagnosis with high accuracy. On a significantly large imaging dataset consisting of over 800 subjects taken from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, classification-success indexes of up to 99.2% are reached with a single measurement.

Paper Details

Date Published: 24 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903524 (24 March 2014); doi: 10.1117/12.2043943
Show Author Affiliations
Fernando Yepes-Calderon, Children’s Hospital Los Angeles (United States)
Univ. de Barcelona (Spain)
Fabian Pedregosa, INRIA Saclay - Île-de-France (France)
Bertrand Thirion, INRIA Saclay - Île-de-France (France)
Yalin Wang, Arizona State Univ. (United States)
Natasha Lepore, Univ. of Southern California (United States)
Children's Hospital Los Angeles (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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