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

Fernando Yepes-Calderon, Fabian Pedregosa, Bertrand Thirion, Yalin Wang, Natasha Lepore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationComputer-Aided Diagnosis
PublisherSPIE
ISBN (Print)9780819498281
DOIs
StatePublished - Jan 1 2014
EventMedical Imaging 2014: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 18 2014Feb 20 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9035
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2014: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego, CA
Period2/18/142/20/14

Keywords

  • Alzheimer's disease
  • Fast clinical diagnosis
  • Machine learning
  • Mild cognitive impairment
  • Support vector machines

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Yepes-Calderon, F., Pedregosa, F., Thirion, B., Wang, Y., & Lepore, N. (2014). Automatic pathology classification using a single feature machine learning support - Vector machines. In Medical Imaging 2014: Computer-Aided Diagnosis [903524] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9035). SPIE. https://doi.org/10.1117/12.2043943