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

Fernando Yepes (1) , Fabian Pedregosa (1) , Bertrand Thirion (1, 2) , Yalin Wang (3) , Natasha Lepore (4)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 NEUROSPIN - Service NEUROSPIN
3 ASU - Arizona State University [Tempe]
4 USC - University of Southern California

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.

Mots clés: Machine learning; Pathology Diseases and disorders; Magnetic resonance imaging; Medical diagnostics; Neuroimaging Algorithms; Brain Databases Diagnostics

Domaines: Imagerie; Machine Learning [stat.ML]

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⟩