使用 HG-PSO 和基于 YOLOv7 的卷积网络实时进行皮肤病变分类
Hera Shaheen1, Maheshwari Prasad Singh1
1Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, India.
概括
这项研究引入了一种新的混合优化和深度学习方法,用于从皮肤镜像中准确地分类皮肤癌. 该方法增强了早期检测和诊断,改善了临床环境中的患者结果.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 皮肤癌的诊断依赖于视觉检查和皮肤镜检查,要求精确的病变定位和分类.
- 早期和准确的诊断对于有效的治疗和改善患者预后至关重要.
- 现有的方法在分类皮肤病变方面达到高准确性和效率方面面临挑战.
研究的目的:
- 开发和评估一种混合优化和深度学习模型,用于增强皮肤癌分类.
- 为了提高皮肤镜像识别和分类皮肤病变的准确性,精度和回忆力.
- 评估方法的性能与各种数据集上的最新技术对比.
主要方法:
- 提出了一种混合方法,将遗传和粒子群优化 (HG-PSO) 与你只看一次版本7 (YOLOv7) 卷积神经网络相结合.
- 优化的YOLOv7被用于初始病变检测,随后是分段的颜色值.
- 然后使用拟议的卷积网络对细分区域进行了分类.
主要成果:
- 该方法在多个数据集中实现了高性能:HAM10000的准确性为98.86%,ISIC-2019的准确性为97.10%,PH2.7的准确性为97.7%.
- 记录了出色的平均精度,回忆和F1分数,证明了强大的分类能力.
- 该模型表现出快速处理时间 (2-3秒),适合远程医疗等实时应用.
结论:
- 提出的基于HG-PSO和YOLOv7的方法在自动皮肤癌分类方面取得了重大进展.
- 它的高精度和效率使其成为早期发现和诊断皮肤癌的有希望的工具.
- 该方法的速度和性能表明,它有可能融入临床工作流程和远程医疗平台.
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