TY - GEN
T1 - Automatic pathology classification using a single feature machine learning support - Vector machines
AU - Yepes-Calderon, Fernando
AU - Pedregosa, Fabian
AU - Thirion, Bertrand
AU - Wang, Yalin
AU - Lepore, Natasha
PY - 2014
Y1 - 2014
N2 - 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 Alzheimera'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 Alzheimera's Disease Neuroimaging Initiative (ADNI) database, classification-success indexes of up to 99.2% are reached with a single measurement.
AB - 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 Alzheimera'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 Alzheimera's Disease Neuroimaging Initiative (ADNI) database, classification-success indexes of up to 99.2% are reached with a single measurement.
KW - Alzheimer's disease
KW - Fast clinical diagnosis
KW - Machine learning
KW - Mild cognitive impairment
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84902105317&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902105317&partnerID=8YFLogxK
U2 - 10.1117/12.2043943
DO - 10.1117/12.2043943
M3 - Conference contribution
AN - SCOPUS:84902105317
SN - 9780819498281
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2014
PB - SPIE
T2 - Medical Imaging 2014: Computer-Aided Diagnosis
Y2 - 18 February 2014 through 20 February 2014
ER -