Bone scintigraphy based on deep learning model and modified growth optimizer
Abstract Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-73991-8 |
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| author | Omnia Magdy Mohamed Abd Elaziz Abdelghani Dahou Ahmed A. Ewees Ahmed Elgarayhi Mohammed Sallah |
| author_facet | Omnia Magdy Mohamed Abd Elaziz Abdelghani Dahou Ahmed A. Ewees Ahmed Elgarayhi Mohammed Sallah |
| author_sort | Omnia Magdy |
| collection | DOAJ |
| description | Abstract Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming. To address the aforementioned issues, this work proposed a machine-learning technique that uses phases to detect Bone scintigraphy. The first phase in the proposed model is the feature extraction and it was conducted based on integrating the Mobile Vision Transformer (MobileViT) model in our framework to capture highly complex representations from raw medical imagery using two primary components including ViT and lightweight CNN featuring a limited number of parameters. In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. Additionally, the applicability of Bone scintigraphy for real-world application is evaluated using 2800 bone scan images (1400 normal and 1400 abnormal). The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study. |
| format | Article |
| id | doaj-art-cbb3db944b5f4e19bb57bf5739580dad |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cbb3db944b5f4e19bb57bf5739580dad2025-08-20T03:05:23ZengNature PortfolioScientific Reports2045-23222024-10-0114111710.1038/s41598-024-73991-8Bone scintigraphy based on deep learning model and modified growth optimizerOmnia Magdy0Mohamed Abd Elaziz1Abdelghani Dahou2Ahmed A. Ewees3Ahmed Elgarayhi4Mohammed Sallah5Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura UniversityDepartment of Mathematics, Faculty of Science, Zagazig UniversityMathematics and Computer Science department, University of Ahmed DRAIADepartment of Information System, College of Computing and Information Technology, University of BishaApplied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura UniversityDepartment of Physics, College of Sciences, University of BishaAbstract Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming. To address the aforementioned issues, this work proposed a machine-learning technique that uses phases to detect Bone scintigraphy. The first phase in the proposed model is the feature extraction and it was conducted based on integrating the Mobile Vision Transformer (MobileViT) model in our framework to capture highly complex representations from raw medical imagery using two primary components including ViT and lightweight CNN featuring a limited number of parameters. In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. Additionally, the applicability of Bone scintigraphy for real-world application is evaluated using 2800 bone scan images (1400 normal and 1400 abnormal). The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study.https://doi.org/10.1038/s41598-024-73991-8Bone scintigraphyBone metastasisGrowth optimizer (GO)Arithmetic optimization algorithm (AOA)Nuclear medicine |
| spellingShingle | Omnia Magdy Mohamed Abd Elaziz Abdelghani Dahou Ahmed A. Ewees Ahmed Elgarayhi Mohammed Sallah Bone scintigraphy based on deep learning model and modified growth optimizer Scientific Reports Bone scintigraphy Bone metastasis Growth optimizer (GO) Arithmetic optimization algorithm (AOA) Nuclear medicine |
| title | Bone scintigraphy based on deep learning model and modified growth optimizer |
| title_full | Bone scintigraphy based on deep learning model and modified growth optimizer |
| title_fullStr | Bone scintigraphy based on deep learning model and modified growth optimizer |
| title_full_unstemmed | Bone scintigraphy based on deep learning model and modified growth optimizer |
| title_short | Bone scintigraphy based on deep learning model and modified growth optimizer |
| title_sort | bone scintigraphy based on deep learning model and modified growth optimizer |
| topic | Bone scintigraphy Bone metastasis Growth optimizer (GO) Arithmetic optimization algorithm (AOA) Nuclear medicine |
| url | https://doi.org/10.1038/s41598-024-73991-8 |
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