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首页> 外文期刊>Future generation computer systems >Machine learning for assisting cervical cancer diagnosis: An ensemble approach
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Machine learning for assisting cervical cancer diagnosis: An ensemble approach

机译：机器学习辅助宫颈癌诊断：一种集成方法

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摘要

Cervical cancer remains one of the most prevalent gynecologic malignancies, worldwide. As cervical cancer is a highly preventable disease, therefore, early screening represents the most effective strategy to minimize the global burden of cervical cancer. However, due to scarce awareness, lack of access to medical centers, and highly expensive procedures in developing countries, the vulnerable patient populations cannot afford to undergo examination regularly. A novel ensemble approach is presented in this paper to predict the risk of cervical cancer. By adopting a voting strategy, this method addresses the challenges associated with previous studies on cervical cancer. A data correction mechanism is proposed to improve the performance of the prediction. A gene-assistance module is also included as an optional strategy to enhance the robustness of the prediction. Multiple measurements are performed to evaluate the proposed method. The results indicate that the likelihood of developing cervical cancer can be effectively predicted using the voting strategy. Compared with other methods, the proposed method is more scalable and practical.
机译：宫颈癌仍然是全球最普遍的妇科恶性肿瘤之一。由于宫颈癌是一种高度可预防的疾病，因此，尽早筛查是使宫颈癌的全球负担最小化的最有效策略。但是，由于缺乏认识，无法进入医疗中心以及发展中国家的医疗程序昂贵，脆弱的患者人群无法负担得起定期检查的费用。本文提出了一种新颖的集成方法来预测子宫颈癌的风险。通过采用表决策略，此方法解决了与先前有关宫颈癌的研究相关的挑战。提出了一种数据校正机制来提高预测性能。基因辅助模块也作为可选策略包括在内，以增强预测的鲁棒性。进行多次测量以评估提出的方法。结果表明，使用投票策略可以有效预测发生宫颈癌的可能性。与其他方法相比，该方法具有更好的可扩展性和实用性。

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  • 来源
    《Future generation computer systems》 |2020年第5期|199-205|共7页
  • 作者

    Jiayi Lu; Enmin Song; Ahmed Ghoneim; Mubarak Alrashoud;

  • 作者单位

    School of Computer Science and Technology. Huazhong University of Science and Technology Wuhan. China;

    Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Egypt;

    Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cervical cancer; Machine learning;

    机译：宫颈癌;机器学习;

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