基于深度学习的高颜色皮肤疾病的图像分类和辅助诊断系统
Jianyun Lu1, Xiaoliang Tong1, Hongping Wu2
1Department of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, PR China.
Heliyon
|October 9, 2023
概括
移动网在诊断多颜色皮肤疾病 (如黑色素瘤) 中表现有前途. 这种深度学习模型实现了高精度和AUC,使其适合临床诊断系统.
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:
- 过度颜色的皮肤状况,包括黑色素 (ML),状肌 (NZ),雀斑 (FC),咖啡牛奶斑点 (CS),奥塔 (NO) 和单眼 (LS) 是常见的皮肤病问题.
- 准确诊断这些疾病对于有效治疗至关重要,但需要客观的分类方法.
- 深度学习 (DL) 提供了分析医疗图像和协助皮肤疾病客观分类的潜力.
研究的目的:
- 评估和确定最优的深度学习算法用于常见的多颜色皮肤疾病的辅助诊断.
- 为了比较十种不同的预训练深度学习模型的性能,用于分类六种多颜色皮肤疾病的图像.
- 确定最适合的DL算法,用于开发超颜色皮肤在线临床诊断系统.
主要方法:
- 我们使用了十个预训练的深度学习模型 (VGG-19,GoogleLeNet,InceptionV3,ResNet50V2,ResNet101V2,ResNet152V2,InceptionResNetV2,DesseNet201,MobileNet,NASNetMobile) 进行研究.
- 这些模型被训练和测试在一组图像数据集上,这些图像代表了六种常见的多颜色皮肤疾病.
- 使用精度和曲线下的面积 (AUC) 度量来评估性能.
主要成果:
- 移动网络显示了99.52%的高训练精度和0.93.9的AUC.
- 虽然测试组的准确性在不同模型之间有所不同,但MobileNet显示出具有竞争力的性能.
- 在评估的算法中,训练时间和参数数量存在显著差异.
结论:
- 移动网络成为一个有前途的深度学习算法,用于辅助诊断多颜色皮肤疾病.
- 它的性能指标表明,在线诊断系统中的临床应用具有很强的潜力.
- 建议进一步开发和验证,将MobileNet集成到临床实践中,以改善诊断支持.
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