Bone scintigraphy based on deep learning model and modified growth optimizer.
Arithmetic optimization algorithm (AOA)
Bone metastasis
Bone scintigraphy
Growth optimizer (GO)
Nuclear medicine
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 10 2024
27 10 2024
Historique:
received:
30
04
2024
accepted:
23
09
2024
medline:
28
10
2024
pubmed:
28
10
2024
entrez:
28
10
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39465262
doi: 10.1038/s41598-024-73991-8
pii: 10.1038/s41598-024-73991-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25627Informations de copyright
© 2024. The Author(s).
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