从皮肤镜像中识别多类皮肤病变的新框架,使用深度学习和可解释的人工智能
Naveed Ahmad1, Jamal Hussain Shah1, Muhammad Attique Khan2,3
1Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan.
这项研究引入了一种用于皮肤病变识别的先进框架,通过数据增强和深度学习增强早期黑色素瘤检测. 这种新的方法实现了高精度,同时减少了计算时间,有助于对抗皮肤癌.
科学领域:
- 皮肤病学 皮肤病学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 皮肤癌,特别是黑色素瘤,对全球健康构成重大风险,早期检测对于降低死亡率至关重要.
- 计算机化皮肤病变识别面临的挑战包括不规则的病变外观,低对比度和不平衡的数据集.
研究的目的:
- 为准确和高效的皮肤病变识别开发一个改进的框架.
- 为了解决现有检测黑色素瘤方法的局限性.
主要方法:
- 一个新的框架,集成数据增强,深度转移学习与Xception和ShuffleNet模型,功能融合,以及优化的蝶优化算法来进行功能选择.
- 可解释的人工智能 (AI) 使用GradCAM进行图像区域分析.
主要成果:
- 在ISIC2018数据集上达到99.3%的高准确率,在HAM10000数据集上达到91.5%.
- 与传统的融合方法相比,演示了计算时间的减少.
- 该框架有效地确定了皮肤损伤图像中的重要区域.
结论:
- 拟议的框架在皮肤病变识别准确性和效率方面取得了重大进展.
- 这种方法有望改善早期黑色素瘤检测和患者的治疗结果.
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