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一个高效的深度学习系统,用于自动检测急性淋巴细胞白血病.

Pradeep Kumar Das1, Sukadev Meher2, Adyasha Rath3

  • 1Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal 506004, Telangana, India; School of Electronics Engineering (SENSE), VIT Vellore, Tamil Nadu 632014, India.

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概括

一个新的深度学习系统使用MobileNetV2和ShuffleNet.Net准确检测急性淋巴细胞白血病 (ALL). 这种高效的模型实现了高精度和灵敏度,这对于早期疾病诊断和治疗至关重要.

关键词:
急性淋巴细胞白血病 (Acute Lymphoblastic Leukemia) 是一种严重的疾病.血液癌症 是一种血液癌症.分类 分类 分类 分类.深度学习是一种深度学习.检测 检测 检测 检测 检测

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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:

  • 对急性淋巴细胞白血病 (ALL) 的早期和准确检测对于有效治疗和患者的生存至关重要.
  • 深度学习,特别是转移学习,在医疗图像分析中表现有前途,即使数据有限.

研究的目的:

  • 开发一种基于深度学习的新,高效和准确的白血病检测系统.
  • 提高分辨能力和受感场,以提高分类性能.

主要方法:

  • 一个混合深度学习模型,将MobileNetV2和ShuffleNet结合起来,用于白血病检测.
  • 整合倒置的剩余瓶,深度可分离的卷积和道混,以改善特征歧视.
  • 实验选择一个最佳的值和权重因子来平衡效率和性能.

主要成果:

  • 拟议的框架在ALLIDB1数据集上实现了卓越的检测性能,准确度为99.07%,灵敏度为100%,精度为98.00%.
  • 在ALLIDB2数据集上,该系统的准确率为98.46%,灵敏度为98.46%,精度为98.46%.
  • 该模型在关键性能指标方面表现优于现有方法,包括准确性,精度,灵敏度,特异性和F1评分.

结论:

  • 开发的深度学习系统为白血病检测提供了更快,更准确的方法.
  • 整合MobileNetV2和ShuffleNet,以及优化的参数,显著提高了诊断能力.
  • 这种新的框架有可能改善急性淋巴细胞白血病患者的早期诊断和治疗结果.