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使用显微镜图像进行早期急性淋巴细胞白血病检测的深度学习模型.

Vatsala Anand1, Prabhnoor Bachhal1, Deepika Koundal2,3

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

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概括

一个深度优化的卷积神经网络 (CNN) 有助于在急性淋巴细胞白血病 (ALL) 的早期诊断. 这种AI模型实现了高准确度和精度,为检测这种骨髓癌提供了有前途的工具.

关键词:
急性淋巴细胞白血病 (Acute Lymphoblastic Leukemia) 是一种急性淋巴细胞白血病.卷积神经网络.卷积神经网络.疾病 疾病 疾病血液学上的血液学.

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科学领域:

  • 血液学 血液学 血液学
  • 人工智能的人工智能
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 急性淋巴细胞白血病 (ALL) 是一种严重的骨髓癌,影响儿童和成人.
  • 早期和准确的诊断对于有效的治疗策略和改善患者结果至关重要.
  • 目前的诊断方法需要整合各种临床,形态,细胞遗传和分子数据.

研究的目的:

  • 开发和评估一个深度优化的卷积神经网络 (CNN),用于早期诊断和检测ALL.
  • 在准确性和精确性方面评估拟议的CNN模型的性能.

主要方法:

  • 一个深度优化的CNN模型被设计成五个卷积块和五个最大池层.
  • 该模型使用超参数进行训练和调整,包括30个时代和32个批量大小.
  • 在模型优化方面,Adam 和 Adamax 优化器进行了比较.

主要成果:

  • 深度优化的CNN模型在ALL检测方面表现出高性能.
  • 使用Adam优化器,该模型实现了0.96的精度和0.95.95的精度.
  • 拟议的CNN模型有效地确定了ALL诊断的关键方面.

结论:

  • 深度优化的CNN为急性淋巴细胞白血病的早期和准确诊断提供了强大的方法.
  • 开发的AI模型显示出有很大的潜力,可以帮助临床医生进行风险评估和治疗计划.
  • 进一步的研究和临床试验对于解决ALL治疗中抗药性,复发性和长期毒性等挑战至关重要.