使用改进的DCGAN分类器进行皮肤损伤的合成和分类
Kavita Behara1, Ernest Bhero2, John Terhile Agee2
1Department of Electrical Engineering, Mangosuthu University of Technology, Durban 4031, South Africa.
这项研究引入了改进的深度卷积生成对抗网络 (DCGAN),用于生成合成皮肤病变图像. 该模型在分类良性和恶性皮肤癌方面取得了高准确性,有助于早期检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 皮肤病学 皮肤病学
背景情况:
- 早期发现皮肤癌可显著改善患者的预后.
- 自动诊断技术对于早期发现皮肤病变至关重要.
- 皮肤病变分类的挑战包括有限的注释数据和类不平衡,阻碍了深度学习模型培训.
研究的目的:
- 提出一种新的皮肤病变合成和分类模型,使用一个改进的深度卷积生成对抗网络 (DCGAN).
- 解决皮肤癌数据集中的数据稀缺性和阶级不平衡问题.
- 为了生成高质量的合成皮肤病变图像,以改善自动诊断.
主要方法:
- 开发一个改进的DCGAN模型,用于生成现实的合成皮肤病变图像.
- 图像增强技术的应用,包括缩放,规范化,利,色彩转换和中间波器.
- 使用区分器的最后一层作为二元分类的分类器 (良性与恶性).
- 培训的学习率为0.01和优化的超参数.
主要成果:
- 该DCGAN分类器模型在ISIC2017数据集上取得了卓越的性能.
- 获得了99.38%的准确性,其中99%用于回忆,精度,F1得分和平衡准确性得分 (BAS).
- 在皮肤病变分类方面表现优于现有的最先进的深度学习模型.
结论:
- 拟议的DCGAN分类器有效生成高质量的合成皮肤病变图像.
- 该模型在分类良性和恶性皮肤病变方面表现出高度准确性.
- 这种方法对皮肤病学中基于深度学习的医学图像分析具有重大前景.
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