基于深度学习的皮肤学疾病的分类
1Innovation and Emerging Technologies Center, Digital Government Authority, Saudi Arabia.
Biomedical engineering and computational biology
|August 3, 2023
概括
通过深度学习,MobileNet在对常见的中东皮肤病进行分类时取得了95.7%的准确率. 这项研究强调了卷积神经网络 (CNN) 在皮肤疾病的自动医疗诊断中的潜力.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 皮肤病学 皮肤病学
背景情况:
- 自动医疗诊断越来越重要,支持临床医生使用先进的工具.
- 深度学习 (DL) 和卷积神经网络 (CNN) 是医学图像分析的关键.
- 虽然皮肤癌的研究是广泛的,但其他皮肤学疾病需要进一步的AI驱动的调查.
研究的目的:
- 评估六个CNN架构的性能,以对中东地区普遍存在的皮肤疾病进行分类.
- 通过过和无雾化技术提高图像质量,以提高诊断准确度.
- 确定该地区皮肤病疾病分类中最有效的CNN模型.
主要方法:
- 对六个CNN架构进行了比较分析:VGG16,EfficientNet,InceptionV3,MobileNet,NasNet和ResNet50. 这六个架构的比较分析.
- 图像过和无色化预处理技术的应用,以增强医疗图像.
- 基于对中东最常见的三种皮肤病的分类准确性的绩效评估.
主要成果:
- 与其他评估的CNN架构相比,MobileNet表现出卓越的性能和准确性.
- 对于目标皮肤疾病,MobileNet 实现了 95.7% 的高分类准确率.
- 图像增强技术对CNN模型的表现产生了积极的影响.
结论:
- 移动网络是一种高效的CNN架构,用于中东地区常见皮肤病的自动诊断.
- 这项研究强调了人工智能的潜力,特别是CNN,以改善皮肤病诊断.
- 未来的工作将涉及开发新的深度学习方法和扩展数据集,以更广泛地覆盖疾病.
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