Machine Learning for Diagnosis of Hematologic Diseases in Magnetic Resonance Imaging of Lumbar Spines.

ABSTRACT: We aimed to assess feasibility of a support vector machine (SVM) texture classifier to discriminate pathologic infiltration patterns from the normal bone marrows in MRI. This retrospective study included 467 cases, which were split into a training (n = 360) and a test set (n = 107). A sagittal T1-weighted lumbar spinal MR image was normalized by an intervertebral disk, and bone marrows were segmented. The various kernel functions and SVM input dimensions were experimented to construct the most optimal classifier model. The accuracy and sensitivity increased as the number of training set sizes increased from 180 to 360. The test set was analyzed by SVM and two independent readers, and the accuracy and sensitivity of the SVM classifier, reader 1 and reader 2 were 82.2% and 85.5%, 79.4% and 8

SUBMITTER: Hwang EJ 

PROVIDER: S-EPMC6465258 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature