使用深度学习用于慢性病的医学图像的检测和分类
Bellamgubba Anoch1,2, Latha Parthiban3
1Research Scholar, Department of Computer Science, Pondicherry University, Pondicherry, India. anoch508@gmail.com.
International urology and nephrology
|September 24, 2025
概括
这项研究引入了一种新的AI方法,使用卷积神经网络和Crow Search从医学图像中检测慢性病 (CKD) 的早期发现,实现高诊断精度.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 腎病學 診斷 診斷 腎病學
背景情况:
- 慢性病 (CKD) 构成一个重大的全球健康挑战,需要及早检测以防止进展到末期病.
- 目前对CKD的诊断方法通常是侵入性的,耗时的,昂贵的,突出了对先进的自动化解决方案的需求.
- 开发精确和高效的诊断工具对于及时干预和改善CKD管理中的患者结果至关重要.
研究的目的:
- 开发和验证一种基于人工智能的新技术,用于使用医学图像准确识别和分类慢性病 (CKD).
- 利用深度学习,特别是卷积神经网络 (CNN),与群众搜索算法 (CSA) 结合,以提高诊断性能.
- 为早期CKD检测建立一个可扩展和自动化的解决方案,改善传统诊断局限性.
主要方法:
- 一种混合方法,将卷积神经网络 (CNN) 与群众搜索算法 (CSA) 集成,用于从医疗图像中对CKD进行分类.
- 实施先进的数据预处理技术,包括Z-score标准化,min-max规范化和强大的扩展.
- 使用奇平方测试进行特征选择,然后通过CSA进行优化,以提高分类准确性和模型有效性.
主要成果:
- 拟议的基于CNN的CS模型在脏CT扫描数据集上取得了卓越的性能,准确率为99.05%.
- 该模型的诊断精度很高,AUC-ROC为99.03%,PR-AUC为99.01%,精度为99.04%,回忆率为99.02%,F1得分为99.00%.
- 结果表明,与传统的机器学习技术相比,诊断的准确性和效率更高,CSA优化了功能选择,以减少冗余和改进可解释性.
结论:
- 基于CNN的CS方法提供了一个高度准确和有效的工具,用于从医学图像中自动诊断CKD.
- 集成CSA用于功能优化,提高模型性能和可解释性,使其成为人工智能驱动的医学诊断的一个有前途的进步.
- 这种方法为慢性病的早期检测和管理提供了可扩展的解决方案,有可能改变患者护理途径.
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