对基于深度学习的自动化癌症诊断模型进行系统审查
Ritu Tandon1, Shweta Agrawal2, Narendra Pal Singh Rathore3
1SAGE University, Indore, India.
Journal of cellular and molecular medicine
|March 1, 2024
概括
深度学习模型对自动化癌症诊断,特别是卷积神经网络有前途. 本次审查强调了它们的有效性,并确定了早期癌症检测未来研究的领域.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
背景情况:
- 深度学习 (DL) 在各种应用中越来越重要.
- 研究人员广泛使用DL模型进行癌症患者自动诊断.
- 癌症诊断仍然是推动医疗保健技术发展的关键领域.
研究的目的:
- 系统地审查用于自动化癌症诊断的深度学习模型.
- 分析用于乳腺,肺,肝脏,大脑和宫癌的DL模型.
- 为了比较不同DL模型在早期癌症检测中的有效性.
主要方法:
- 对2016年至2022年间发表的研究文章进行系统审查.
- 识别和分类用于癌症诊断的各种深度学习模型.
- 对早期癌症检测模型性能进行比较分析.
主要成果:
- 卷积神经网络 (CNN) 模型在自动化癌症诊断中表现出明显的准确性.
- 预先训练的模型经常被用于提高诊断性能.
- 审查发现了当前基于DL的自动化癌症诊断系统中的几个缺陷.
结论:
- 深度学习,特别是CNN,为准确和早期的癌症诊断提供了巨大的潜力.
- 需要进一步的研究来解决现有的局限性,并提高DL模型的稳定性.
- 未来的方向包括增强DL模型,以便更有效地自动检测癌症.
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