24 March 2014 Automatic pathology classification using a single feature machine learning support - vector machines
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Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903524 (2014); doi: 10.1117/12.2043943
Event: SPIE Medical Imaging, 2014, San Diego, California, United States
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.
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Fernando Yepes-Calderon, Fabian Pedregosa, Bertrand Thirion, Yalin Wang, Natasha Lepore, "Automatic pathology classification using a single feature machine learning support - vector machines", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903524 (24 March 2014); doi: 10.1117/12.2043943; https://doi.org/10.1117/12.2043943
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KEYWORDS
Brain

Machine learning

Principal component analysis

Alzheimer's disease

Magnetic resonance imaging

Diagnostics

Pathology

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