A machine learning approach for specification of spinal cord injuries using fractional anisotropy values obtained from diffusion tensor images.
Bunheang TayJung Keun HyunSejong OhPublished in: Computational and mathematical methods in medicine (2014)
Diffusion Tensor Imaging (DTI) uses in vivo images that describe extracellular structures by measuring the diffusion of water molecules. These images capture axonal movement and orientation using echo-planar imaging and provide critical information for evaluating lesions and structural damage in the central nervous system. This information can be used for prediction of Spinal Cord Injuries (SCIs) and for assessment of patients who are recovering from such injuries. In this paper, we propose a classification scheme for identifying healthy individuals and patients. In the proposed scheme, a dataset is first constructed from DTI images, after which the constructed dataset undergoes feature selection and classification. The experiment results show that the proposed scheme aids in the diagnosis of SCIs.
Keyphrases
- deep learning
- machine learning
- spinal cord
- end stage renal disease
- convolutional neural network
- ejection fraction
- newly diagnosed
- chronic kidney disease
- optical coherence tomography
- prognostic factors
- high resolution
- peritoneal dialysis
- healthcare
- oxidative stress
- wastewater treatment
- magnetic resonance imaging
- white matter
- computed tomography
- patient reported outcomes
- neuropathic pain
- big data
- cerebrospinal fluid
- multiple sclerosis
- contrast enhanced