Bone scintigraphy based on deep learning model and modified growth optimizer.

Autor: Magdy O; Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt., Elaziz MA; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt. abd_el_aziz_m@yahoo.com.; Faculty of Computer Science and Engineering, Galala University, Suze, 435611, Egypt. abd_el_aziz_m@yahoo.com.; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates. abd_el_aziz_m@yahoo.com., Dahou A; Mathematics and Computer Science department, University of Ahmed DRAIA, Adrar, 01000, Algeria.; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China., Ewees AA; Department of Information System, College of Computing and Information Technology, University of Bisha, P.O Box 551, Bisha, 61922, Saudi Arabia., Elgarayhi A; Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt., Sallah M; Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha, 61922, Saudi Arabia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 27; Vol. 14 (1), pp. 25627. Date of Electronic Publication: 2024 Oct 27.
DOI: 10.1038/s41598-024-73991-8
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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