利用 mRMR + SS0 + WSVM 改进乳腺癌分类:一种混合方法

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz
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引用次数: 0

摘要

通过组织病理学图像检测乳腺癌既费时又费力。加快早期检测对于及时进行医疗干预至关重要。对微阵列数据进行精确分类面临着维度和噪声的挑战。研究人员使用基因选择技术来解决这一问题。预处理、集合和归一化程序等其他技术旨在提高图像质量。这些技术还能影响分类方法,帮助解决过拟合和数据平衡问题。更复杂的版本有可能在提高分类准确性的同时减少过度拟合。最近的技术进步推动了乳腺癌的自动诊断。这项研究介绍了一种使用 Salp Swarm Optimization(SSO)和支持向量机(SVM)进行基因选择和乳腺肿瘤分类的新方法。这一过程包括两个阶段:mRMR 根据基因的相关性和独特性对基因进行预选,然后使用集成了 SSO 的 WSVM 进行分类。在 SSO 的辅助下,WSVM 会修剪冗余基因并分配权重,从而提高基因的重要性。SSO 还根据基因权重微调内核参数。实验结果展示了 mRMR-SSO-WSVM 方法的有效性,在乳腺基因表达数据集上实现了较高的准确度、精确度、召回率和 F1 分数。具体来说,我们的方法达到了 99.62% 的准确率、100% 的精确率、100% 的召回率和 99.10% 的 F1 分数。与现有方法的比较分析表明了我们的方法的优越性,与传统的基于 SVM 的方法相比,准确率提高了 4%,F1 分数提高了 3.5%。总之,本研究证明了所提出的 mRMR-SSO-WSVM 方法在推进乳腺癌分类方面的潜力,它显著改善了性能指标,并有效解决了过拟合和数据不平衡等挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach

Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach

Detecting breast cancer through histopathological images is time-consuming due to their volume and complexity. Speeding up early detection is crucial for timely medical intervention. Accurately classifying microarray data faces challenges from its dimensionality and noise. Researchers use gene selection techniques to address this issue. Additional techniques like pre-processing, ensemble, and normalization procedures aim to improve image quality. These can also impact classification approaches, helping resolve overfitting and data balance issues. A more sophisticated version could potentially boost classification accuracy while reducing overfitting. Recent technological advances have driven automated breast cancer diagnosis. This research introduces a novel method using Salp Swarm Optimization (SSO) and Support Vector Machines (SVMs) for gene selection and breast tumor classification. The process involves two stages: mRMR preselects genes based on their relevance and distinctiveness, followed by SSO-integrated WSVM for classification. WSVM, aided by SSO, trims redundant genes and assigns weights, enhancing gene significance. SSO also fine-tunes kernel parameters based on gene weights. Experimental results showcase the effectiveness of the mRMR-SSO-WSVM method, achieving high accuracy, precision, recall, and F1-score on breast gene expression datasets. Specifically, our approach achieved an accuracy of 99.62%, precision of 100%, recall of 100%, and an F1-score of 99.10%. Comparative analysis with existing methods demonstrates the superiority of our approach, with a 4% improvement in accuracy and a 3.5% increase in F1-score over traditional SVM-based methods. In conclusion, this study demonstrates the potential of the proposed mRMR-SSO-WSVM methodology in advancing breast cancer classification, offering significant improvements in performance metrics and effectively addressing challenges such as overfitting and data imbalance.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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