肺癌诊断中的深度学习范式:一个方法审查,公开挑战和未来的方向
Aryan Nikul Patel1, Kathiravan Srinivasan1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
概括
深度学习模型对早期肺癌诊断有很大的希望,改善结节检测和预后预测. 这些先进的AI工具可以提高准确性,帮助放射科医生,并可能挽救生命.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 肺癌是全球癌症死亡的主要原因之一.
- 早期诊断对于改善患者的结果至关重要.
- 深度学习 (DL) 在医学诊断方面显示出潜力.
研究的目的:
- 审查深度学习模型在肺癌诊断中的应用.
- 在各种成像模式中评估DL性能.
- 确定肺癌检测中DL的挑战和未来方向.
主要方法:
- 按照PRISMA指南进行的方法审查.
- 对肺癌诊断的深度学习研究进行了全面的搜索和分析.
- 在结节检测,分类和预后预测中评估DL模型.
主要成果:
- 深度学习模型在肺癌诊断方面实现了最先进的性能.
- 深度神经网络在结节检测,恶性瘤分类和预后方面表现出色.
- DL集成提高了诊断准确度,并通过计算机辅助诊断系统帮助放射科医生.
结论:
- 深度学习为提高肺癌诊断精度和效率提供了显著的潜力.
- 解决数据质量和可解释性挑战是充分实现DL影响的关键.
- 持续的研究对于克服障碍和利用AI在瘤学中的变革力量至关重要.
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