Integral University Lucknow, India.
Department of IT, G.L Bajaj Institute of Technology & Management, Greater Noida, India.
Biomed Res Int. 2022 Aug 5;2022:3618197. doi: 10.1155/2022/3618197. eCollection 2022.
Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.
间皮瘤是一种侵袭性和致命性的癌症。它是覆盖患者大部分内部器官的一层薄薄的组织。有治疗方法可用；然而，对于大多数患者来说，无法治愈。因此，正在进行大量的研究，使用各种不同的方法来检测间皮瘤癌症；但本文侧重于优化技术，以优化生物医学图像来检测癌症。由于医学领域的样本数量有限，提出了一种 Relief-PSO 头和间皮瘤颈部癌症病理图像特征选择方法。该方法降低了多级维数。首先， Relief 技术根据特征与类别之间的关系选择不同的特征权重。其次，建议使用混合二进制粒子群优化 (HBPSO) 自动确定候选特征子集的最佳特征子集。该技术在形态特征筛选、降维和分类性能方面优于其他七种特征选择算法。
