Research on Bone Age Automatic Judgment Algorithm Based on Deep Learning and Hand X-ray Image
Objective: To learn the depth of paper using feature extraction, combined with critical areas of heat syndrome and related information, X-ray image of hand to analyze bone age children. Methods: The thesis of the X-ray image data preprocessing left hand, the use of depth
of depth neural network learning methods, combined with clinical data skeletal age evaluation model to evaluate the effectiveness of the test model. Results: X-ray image of hand artificial feature extraction, combined SVM classification, automatic assessment of skeletal age. The method
of automatic assessment of bone age SVM-based feature primarily artificial, SIFT features extracted image, LBP features, characteristics of GCLM, these features are combined, and then used to train the SVM, have some ability to automatically assess bone age assessment based on SVM. Conclusion:
This topic X-ray image based on the hand bones, computer vision, machine learning to extract the relevant methods, pretreatment and segmentation of X-ray images of the hand bones, characterized by automatic assessment of bone age, lack the core image of the sample problem.
Keywords: BONE AGE AUTOMATIC JUDGMENT ALGORITHM; DEEP LEARNING; X-RAY IMAGE
Document Type: Research Article
Publication date: 01 January 2021
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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