Double-bone-age evaluation method based on joint global and local convolutional neural network features
A convolutional neural network and local feature technology, applied in the field of image processing, can solve the problem of low accuracy of bone age assessment, achieve high accuracy, concise and easy-to-understand purpose and advantages, and improve performance
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Embodiment 1
[0033] The embodiment of the present application provides a double bone age assessment method based on joint global and local convolutional neural network features, refer to figure 1 , the method includes the following steps S101 to S106:
[0034] S101. Obtain a full picture of the hand bone corresponding to the tester, gender information, and an epiphyseal region image corresponding to each epiphyseal region to be rated extracted from the full hand bone picture.
[0035] S102. Construct a double-bone age prediction model combining global and local convolutional neural network features. The double-bone age prediction model includes a first convolutional neural network, a second convolutional neural network, and a GCN network, and the first convolutional neural network is used for the whole image Feature extraction, the second convolutional neural network and GCN network are used for local feature extraction, and the double-bone age prediction model is gradually and alternately...
Embodiment 2
[0091] Based on the same idea, refer to Figure 4 , this application also proposes a bone age assessment device based on a multi-task convolutional neural network, including:
[0092] The acquiring module 401 is used to acquire the full picture of the hand bone corresponding to the tester, the gender information, and the image of the epiphyseal region corresponding to each epiphyseal region to be rated extracted from the full picture of the hand bone;
[0093] The model building module 402 is used to construct a double-bone age prediction model combining global and local convolutional neural network features. The double-bone age prediction model includes a first convolutional neural network, a second convolutional neural network and a GCN network, and the first convolutional neural network. The network is used for full-image feature extraction, the second convolutional neural network and GCN network are used for local feature extraction, and the double-bone age prediction mode...
Embodiment 3
[0101] This embodiment also provides an electronic device, refer to Figure 5 , comprising a memory 504 and a processor 502, where a computer program is stored in the memory 504, and the processor 502 is configured to run the computer program to perform the steps in any of the above method embodiments.
[0102] Specifically, the above-mentioned processor 502 may include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
[0103] Among others, memory 504 may include mass storage 504 for data or instructions. By way of example and not limitation, the memory 504 may include a hard disk drive (Hard Disk Drive, referred to as HDD), floppy disk drive, solid state drive (Solid State Drive, referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (Uni...
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