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相关实验视频

Shahbaz Sikandar1, Rabbia Mahum1, Adham E Ragab2

  • 1Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan.

Diagnostics (Basel, Switzerland)
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

一个新的卷积神经网络 (CNN),SCDet,准确地检测到微小的皮肤癌. 这种方法提供了高精度和回忆,改善了早期皮肤癌诊断和患者存活率.

关键词:
批量规范化的批量规范化良性 良性的卷积神经网络的神经网络.皮肤显微镜的图像恶性恶性 恶性恶性马克斯游泳池的最大数量皮肤癌是皮肤癌.皮肤病变 皮肤病变软max 是一个软max.

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科学领域:

  • 皮肤病学 皮肤病学
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 皮肤癌以不规则的病变为特征,在晚期阶段具有显著的死亡风险.
  • 早期发现皮肤癌对于改善患者生存率至关重要.
  • 现有的诊断方法可能难以识别最小的瘤,需要改进的检测技术.

研究的目的:

  • 通过使用新型深度学习模型,提出一种用于早期皮肤癌诊断的强大而准确的方法.
  • 开发一种卷积神经网络 (CNN),能够检测其他方法可能错过的微小皮肤病变.

主要方法:

  • 一个名为SCDet的32层卷积神经网络 (CNN) 为皮肤病变检测而开发.
  • 该模型处理 227x227 图像,使用对卷积层,批量规范化和 ReLU 层进行图案提取.
  • 通过使用包括精度,回忆,灵敏度,特异性和准确性在内的关键性能指标进行SCDet的培训和评估.

主要成果:

  • SCDet实现了高性能指标:99.2%的精度,100%的回忆,100%的灵敏度,99.20%的特异性和99.6%的准确性.
  • 拟议的SCDet模型在检测微小皮肤瘤的准确性和精度上优于预先训练的VGG16,AlexNet和SqueezeNet等模型.
  • 由于其较浅的架构,SCDet与ResNet50等模型相比显示出更高的速度和更低的计算成本.

结论:

  • SCDet为皮肤癌的早期诊断提供了一个高效和高效的深度学习解决方案.
  • 该模型能够高精度地检测到最小的病变,这显著提高了诊断能力.
  • 通过早期干预,SCDet为皮肤癌检测提供了一个计算成本有效的替代方案,有望通过早期干预改善患者的治疗结果.