Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems.

IF 1.6 Q4 ONCOLOGY
Pennipat Nabheerong, Warissara Kiththiworaphongkich, Watcharaporn Cholamjiak
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引用次数: 0

Abstract

To detect breast cancer in mammography screening practice, we modify the inertial relaxed CQ algorithm with Mann's iteration for solving split feasibility problems in real Hilbert spaces to apply in an extreme learning machine as an optimizer. Weak convergence of the proposed algorithm is proved under certain mild conditions. Moreover, we present the advantage of our algorithm by comparing it with existing machine learning methods. The highest performance value of 85.03% accuracy, 82.56% precision, 87.65% recall, and 85.03% F1-score show that our algorithm performs better than the other machine learning models.

Abstract Image

Abstract Image

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使用改进的惯性投影算法进行乳腺癌筛查的分割可行性问题。
为了在乳腺x线摄影筛查实践中检测乳腺癌,我们改进了Mann迭代求解实际Hilbert空间中分裂可行性问题的惯性松弛CQ算法,将其作为优化器应用于极限学习机。在一定的温和条件下证明了该算法的弱收敛性。此外,通过与现有机器学习方法的比较,我们展示了我们算法的优势。85.03%的准确率、82.56%的精度、87.65%的召回率和85.03%的f1分数的最高性能值表明,我们的算法比其他机器学习模型性能更好。
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来源期刊
CiteScore
3.40
自引率
0.00%
发文量
25
审稿时长
19 weeks
期刊介绍: International Journal of Breast Cancer is a peer-reviewed, Open Access journal that provides a forum for scientists, clinicians, and health care professionals working in breast cancer research and management. The journal publishes original research articles, review articles, and clinical studies related to molecular pathology, genomics, genetic predisposition, screening and diagnosis, disease markers, drug sensitivity and resistance, as well as novel therapies, with a specific focus on molecular targeted agents and immune therapies.
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