DL-PCMNet:分布式学习启用平行卷积记忆网络,用于用皮肤镜图像进行皮肤癌分类
Afnan M Alhassan1, Nouf I Altmami1
1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
一个新的分布式学习启用并行卷积记忆网络 (DL-PCMNet) 模型使用深度学习准确地分类皮肤癌. 这种方法克服了现有的皮肤病变分类技术的局限性,提高了诊断准确度.
科学领域:
- 皮肤病学和医学成像学
- 人工智能在医学中的应用
- 计算病理学计算病理学
背景情况:
- 皮肤癌是一种快速传播的致命疾病,其特点是皮肤细胞异常生长.
- 从皮肤镜像中分类皮肤病变和诊断瘤存在重大挑战.
- 现有的诊断方法存在数据不足,计算复杂性,类不平衡和性能差等问题.
研究的目的:
- 为了引入一个先进的模型,有效地分类皮肤癌.
- 解决当前方法在准确性和可靠性方面的局限性.
- 通过深度学习改进皮肤病变的诊断.
主要方法:
- 分布式学习启用并行卷积记忆网络 (DL-PCMNet) 模型的开发.
- 整合分布式学习以提高灵活性和可靠性.
- 结合卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 进行强大的特征提取和依赖性捕获.
- 应用先进的预加工和特征提取技术.
主要成果:
- 在ISIC 2019数据集上,DL-PCMNet模型实现了高性能.
- 在90%的训练中实现了97.28%的准确性,97.30%的精度,97.17%的灵敏度和97.72%的特异性.
- 与现有的皮肤癌分类模型相比,其表现优越.
结论:
- 拟议的DL-PCMNet模型为皮肤癌分类提供了有效和准确的解决方案.
- 这种深度学习方法有效地克服了以前的诊断挑战.
- 该模型显示了改善皮肤病诊断的巨大潜力.
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