Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt.
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt.
Sci Rep. 2024 Oct 27;14(1):25627. doi: 10.1038/s41598-024-73991-8.
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.
骨闪烁扫描被认为是全身骨转移筛查的有效诊断方法。目前，全身骨扫描图像分析主要依赖于核医学医生的手动阅读。然而，手动分析需要丰富的经验，既紧张又耗时。针对上述问题，本研究提出了一种基于相位的机器学习技术，用于检测骨闪烁扫描。所提出模型的第一阶段是特征提取，它是基于在我们的框架中集成 Mobile Vision Transformer (MobileViT) 模型，使用两个主要组件，即 ViT 和轻量级 CNN 来捕获来自原始医学图像的高度复杂表示，其中 CNN 具有有限数量的参数。此外，第二阶段称为特征选择，它依赖于算术优化算法 (AOA) 用于改进 Growth Optimizer (GO)。我们使用一组 18 个 UCI 数据集来评估所提出的 FS 模型，名为 GOAOA 的性能。此外，还使用 2800 张骨扫描图像（1400 张正常和 1400 张异常）评估骨闪烁扫描在实际应用中的适用性。结果和统计分析表明，所提出的 GOAOA 算法作为 FS 技术优于本研究中使用的其他 FS 算法。
