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Updated: Jan 11, 2026

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优化了CNN框架用于使用Otsu基于值的图像细分来检测疟疾.

Retinderdeep Singh1, Chander Prabha2, Shahab Abdulla3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140417, India.

Scientific reports
|November 17, 2025
PubMed
概括
此摘要是机器生成的。

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这项研究使用Otsu值细分的深度学习框架增强了疟疾检测,通过血液涂抹图像实现了97.96%的早期诊断准确度.

科学领域:

  • 医疗成像医学成像
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 从血液涂抹中准确诊断疟疾至关重要,但具有挑战性,特别是在资源有限的地区.
  • 目前的方法往往在敏感性和特异性方面扎,影响患者的结果.

研究的目的:

  • 开发一个优化的深度学习框架,以改善疟疾感染细胞的检测.
  • 通过将基于Otsu值的图像细分与卷积神经网络 (CNN) 集成来提高诊断准确性.

主要方法:

  • 开发了一种混合并行功能融合模型,将12层CNN和EfficientNet-B7结合起来.
  • 图像细分时使用Otsu值来强调寄生虫相关的区域.
  • 用于培训和测试的数据集包括43,400张血液涂抹图像,并为验证使用手动注释.

主要成果:

  • 基线CNN实现了95%的准确性,通过EfficientNet-B7集成改进到97%.
  • 通过Otsu细分增强的CNN达到97.96%的峰值准确度.
  • 细分指标显示高性能 (Dice: 0.848, IoU: 0.738),验证了Otsu的有效性.

结论:

关键词:
血细胞是血液中的细胞.在美国,CNN是CNN.分类 分类 分类 分类.有效的网 效率的网疟疾:疟疾是一种疾病.在Otsu的门上.分段化 分段化 分段化 分段化

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  • 通过Otsu细分的简单预处理显著提高了CNN在疟疾诊断方面的性能.
  • 拟议的框架为疟疾检测提供了一个可靠,可扩展和计算可行的工具.
  • 由细分驱动的深度学习显示出具有成本效益的疟疾诊断解决方案的前景.