VIRD: An ensemble transfer learning to improve automatic stratification of types of lumbar intervertebral disc degeneration

Abstract The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single- (e.g., disc herniation, disc prolapse, or disc bulge) and comorbidity-type degeneration (i.e., simultaneous presence of two or more conditions of disc herniation, prolapse, and bulge in a patient) respectively. The degenerative changes of the lumbar spine differentiate in the level of severity and type. A sample of lumbar magnetic resonance images from multiple clinical hospitals in China was collected and used in the proposal assessment. Theoretically, we devised a novel transfer learning framework VIRD by ensembling four pre-trained models including Densenet169, ResNet101, InceptionResNetV2, and VGG16. Thereafter, the proposed approach was applied to the clinical data and achieved 99% accuracy versus 94%, 96%, 96%, 96%, and 98% for compositional benchmark models of VGG16, InceptionResNetV2, DenseNet169, ResNet101, and other ensemble deep learning respectively. Furthermore, improved performance was observed as well for the metric of the area under the curve, producing a 1% increase relative to other ensemble learning, a 2% increase relative to most-studied models, and a 3% increase relative to the baseline models. The novel methodology can be used as a guide in the initial and efficient screening of complex degeneration of lumbar intervertebral discs and assist in the early-stage selection of clinically differentiated treatment options.