Research on an intelligent evaluation method of bone age based on multi-region combination
Bone age is one of the most important evaluation indexes for the growth and development of children and adolescents. The bone age assessment method based on deep learning generally uses the whole left wrist X-ray film or some regions of interest in the left wrist X-ray film. Based on the entire X-ra...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2023.2233545 |
| _version_ | 1797447501568016384 |
|---|---|
| author | Kaiyan Chen Jianan Wu Yan Mao Wei Lu Keji Mao Wenxiu He |
| author_facet | Kaiyan Chen Jianan Wu Yan Mao Wei Lu Keji Mao Wenxiu He |
| author_sort | Kaiyan Chen |
| collection | DOAJ |
| description | Bone age is one of the most important evaluation indexes for the growth and development of children and adolescents. The bone age assessment method based on deep learning generally uses the whole left wrist X-ray film or some regions of interest in the left wrist X-ray film. Based on the entire X-ray film, the intelligent evaluation process is simple, but the accuracy is low. Although intelligent evaluation based on regions of interest has high accuracy, it requires prior knowledge and the process is complex. To solve the above problems, this paper proposes a multi-region combined method for bone age assessment. A small number of regions of interest in wrist bone X-ray films are extracted, and then the whole X-ray film and these regions of interest were used to evaluate the bone age. The experiment uses the improved Inception-ResNet-V2 convolutional neural network. The results show that compared with other bone age assessment studies on the open data set published by the North American radiological Association, this method can obtain higher accuracy of bone age assessment, with an average absolute error of 7.11 months. This method improves the efficiency and accuracy of bone age assessment while simplifying the assessment process. |
| first_indexed | 2024-03-09T13:56:56Z |
| format | Article |
| id | doaj.art-4ec10e17546a491a947358a4a748381c |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-03-09T13:56:56Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-4ec10e17546a491a947358a4a748381c2023-11-30T12:45:32ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832023-12-0111110.1080/21642583.2023.2233545Research on an intelligent evaluation method of bone age based on multi-region combinationKaiyan Chen0Jianan Wu1Yan Mao2Wei Lu3Keji Mao4Wenxiu He5College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Zhijiang, Zhejiang University of Technology, Shaoxing, People’s Republic of ChinaBone age is one of the most important evaluation indexes for the growth and development of children and adolescents. The bone age assessment method based on deep learning generally uses the whole left wrist X-ray film or some regions of interest in the left wrist X-ray film. Based on the entire X-ray film, the intelligent evaluation process is simple, but the accuracy is low. Although intelligent evaluation based on regions of interest has high accuracy, it requires prior knowledge and the process is complex. To solve the above problems, this paper proposes a multi-region combined method for bone age assessment. A small number of regions of interest in wrist bone X-ray films are extracted, and then the whole X-ray film and these regions of interest were used to evaluate the bone age. The experiment uses the improved Inception-ResNet-V2 convolutional neural network. The results show that compared with other bone age assessment studies on the open data set published by the North American radiological Association, this method can obtain higher accuracy of bone age assessment, with an average absolute error of 7.11 months. This method improves the efficiency and accuracy of bone age assessment while simplifying the assessment process.https://www.tandfonline.com/doi/10.1080/21642583.2023.2233545Deep learningconvolutional neural networkbone age assessmentregion of interestimage enhancement |
| spellingShingle | Kaiyan Chen Jianan Wu Yan Mao Wei Lu Keji Mao Wenxiu He Research on an intelligent evaluation method of bone age based on multi-region combination Systems Science & Control Engineering Deep learning convolutional neural network bone age assessment region of interest image enhancement |
| title | Research on an intelligent evaluation method of bone age based on multi-region combination |
| title_full | Research on an intelligent evaluation method of bone age based on multi-region combination |
| title_fullStr | Research on an intelligent evaluation method of bone age based on multi-region combination |
| title_full_unstemmed | Research on an intelligent evaluation method of bone age based on multi-region combination |
| title_short | Research on an intelligent evaluation method of bone age based on multi-region combination |
| title_sort | research on an intelligent evaluation method of bone age based on multi region combination |
| topic | Deep learning convolutional neural network bone age assessment region of interest image enhancement |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2023.2233545 |
| work_keys_str_mv | AT kaiyanchen researchonanintelligentevaluationmethodofboneagebasedonmultiregioncombination AT jiananwu researchonanintelligentevaluationmethodofboneagebasedonmultiregioncombination AT yanmao researchonanintelligentevaluationmethodofboneagebasedonmultiregioncombination AT weilu researchonanintelligentevaluationmethodofboneagebasedonmultiregioncombination AT kejimao researchonanintelligentevaluationmethodofboneagebasedonmultiregioncombination AT wenxiuhe researchonanintelligentevaluationmethodofboneagebasedonmultiregioncombination |