ASSISTANCE DIAGNOSIS METHOD FOR LUMBAR DISEASE BASED ON DEEP LEARNING

摘要

The present invention relates to an ancillary diagnosis method for a lumbar disease based on deep learning which can accurately and automatically diagnose a lumbar disease. The ancillary diagnosis method comprises: (a) a step of using learning data to perform deep learning on lumbar diseases to generate a diagnosis model; (b) a step of inputting a lumbar disease image to be diagnosed; and (c) a step of diagnosing existence of a lumbar disease of the lumbar disease image to be diagnosed based on the diagnosis model. The step (a) includes: (a1) a step of inputting a plurality of lumbar images and segmentation images obtained by segmenting individual lumbar vertebrae for the lumbar images as first learning data; (a2) a step of using the segmentation images as output data to perform deep learning on the first learning data; (a3) a step of generating a segmentation model by deep learning using the first learning data as an input; (a4) a step of inputting a plurality of normal lumbar patches and a plurality of disease lumbar patches as second learning data; (a5) a step of performing deep learning on the second learning data by a preregistered classification algorithm; and (a6) a step of generating a disease classification model by deep learning using the second learning data as an input. In the step (c), the segmentation model and the disease classification model are applied as the diagnosis model.