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下一代皮肤疾病预测方法采用混合深度转移学习

Yonis Gulzar¹, Shivani Agarwal², Saira Soomro³

¹Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa, Saudi Arabia.

Frontiers in big data

|March 6, 2025

概括

一种新的混合深度转移学习方法 (HDTLM) 将皮肤疾病分类准确度提高到98.18%. 这种先进的模型集成了DenseNet121和EfficientNetB0,为皮肤病诊断提供了强大的解决方案.

科学领域:

皮肤病学和医学成像学
医疗保健中的人工智能
计算机视觉用于疾病诊断和诊断

背景情况:

由于复杂的病变和有限的数据,皮肤疾病的分类具有挑战性.
传统的深度学习模型在皮肤学应用中难以实现泛化.
准确的自动诊断对于有效的患者护理和治疗至关重要.

研究的目的:

开发一种改进的皮肤疾病分类方法.
为了解决传统的卷积神经网络 (CNN) 的局限性.
提出混合深度转移学习方法 (HDTLM) 以提高预测准确度.

主要方法:

将DenseNet121和EfficientNetB0集成到一个混合模型 (HDTLM).
在19种皮肤疾病中的19171张图像数据集上进行培训和验证.
与包括VGG19,MobileNetV2和AlexNet在内的最先进模型进行比较分析.

主要成果:

HDTLM实现了98.18%的培训准确率和97.57%的验证准确率.
该模型在精度 (0.95),回忆 (0.96) 和F1得分 (0.95) 方面表现出卓越的性能.
在关键绩效指标中始终优于基线模型的表现.

结论:

HDTLM有效地提高了皮肤疾病的分类,特别是有限的数据.
混合方法利用互补的优势,实现强大可扩展的自动诊断.
这种方法为改善皮肤病诊断准确度提供了一个有希望的解决方案.

关键词:

密集的网络121 有效网B0 有效网B0 计算机视觉计算机视觉深度学习是一种深度学习.图像的分类图像的分类.皮肤疾病预测皮肤疾病预测转移学习转移学习

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