Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, exis...

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Veröffentlicht in:Journal of medical systems Jg. 44; H. 1; S. 15 - 7
Hauptverfasser: Liu, Jian, Wang, Jian, Ruan, Weiwei, Lin, Chengshan, Chen, Daguo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2020
Springer Nature B.V
Schlagworte:
Algorithms
Biomedical materials
Bone density
Bone diseases
Bone mineral density
Deep learning
Density measurement
Diagnosis
Diagnostic systems
Distributed Analytics and Deep Learning in Health Care
Entropy (Information theory)
Feature maps
Health Informatics
Health Sciences
Image segmentation
Medical diagnosis
Medical imaging
Medicine
Medicine & Public Health
Normalizing
Object recognition
Osteoporosis
Patient Facing Systems
Statistics for Life Sciences
Deep learning
Osteoporosis
X-ray image
Bone mineral density
Medical diagnosis
U-net model
ISSN:0148-5598, 1573-689X, 1573-689X
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Zusammenfassung:The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1502-3