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. 2023 Sep 12;13(1):15019. doi: 10.1038/s41598-023-41733-x.
This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique's performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.
本文提出了一种基于机器学习的骨闪烁成像解释技术，重点关注特征提取，并引入了一种名为 GJOW 的新特征选择方法。GJOW 通过整合来自鲸鱼优化算法（WOA）的算子来增强金豺优化（GJO）算法的有效性。该技术通过使用 18 个基准数据集和从伽马相机获得的 581 张骨扫描图像（包括 362 个异常和 219 个正常病例）进行了广泛的实验来评估性能。结果突出了 GJOW 算法在骨转移检测中的卓越预测效果，其准确率达到 71.79%，特异性达到 91.14%。本研究的贡献包括引入了一种新的基于机器学习的方法，使用伽马相机扫描来检测骨转移，从而提高了识别骨转移的准确性。这些发现对早期检测和干预具有实际意义，有可能改善患者的预后。
