Automatic pathology classification using a single feature machine learning - support vector machines - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Automatic pathology classification using a single feature machine learning - support vector machines

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) 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.
Fichier principal
Vignette du fichier
Spie2014_Extension.pdf (593.16 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00975872 , version 1 (09-04-2014)

Identifiers

Cite

Fernando Yepes, Fabian Pedregosa, Bertrand Thirion, Yalin Wang, Natasha Lepore. Automatic pathology classification using a single feature machine learning - support vector machines. SPIE Medical Imaging 2014, Feb 2014, San Diego, United States. pp.24, ⟨10.1117/12.2043943⟩. ⟨hal-00975872⟩
432 View
429 Download

Altmetric

Share

Gmail Facebook X LinkedIn More