基于深度学习的皮肤损伤多类分类与全球平均水平的聚合改善
Paravatham V S P Raghavendra1, C Charitha2, K Ghousiya Begum3
1School of Mechanical Engineering, SASTRA Deemed to be University, 613401, Thanjavur, India.
Journal of digital imaging
|July 5, 2023
概括
一个新的深度卷积神经网络 (DCNN) 模型准确地识别和分类皮肤病变. 这种人工智能工具实现了97.20%的准确性,超过了现有的早期皮肤癌检测方法.
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:
- 皮肤癌的诊断依赖于视觉检查和活检,它们在准确性和可访问性方面存在局限性.
- 开发用于皮肤病变分类的自动化系统对于早期检测和改善患者治疗结果至关重要.
研究的目的:
- 提出一种新的深层卷积神经网络 (DCNN) 模型,用于准确的多类皮肤病变识别和分类.
- 通过先进的深度学习技术,提高皮肤癌检测的性能.
主要方法:
- 开发了一种新的DCNN模型,用于皮肤病变分析,该模型结合了全球平均聚合.
- 用HAM10000数据集,包括七个皮肤病变类别,用于模型培训和验证.
- 预处理涉及黑帽过以去除工件和重新采样以进行数据平衡.
主要成果:
- 拟议的DCNN模型在多类皮肤病变分类中实现了最高准确率97.20%.
- 性能与已建立的转移学习模型 (如ResNet50,VGG-16,MobileNetV2和DenseNet121.1) 相比进行了基准测试.
- 模型的有效性通过使用图形用户界面 (GUI) 进行视觉确认.
结论:
- 与现有的最先进的方法相比,开发的DCNN模型在自动化皮肤病变分类方面表现出卓越的性能.
- 这种由人工智能驱动的工具显示出作为皮肤科医生的计算机辅助诊断辅助工具的巨大潜力.
- 这些发现支持在医疗诊断中深度学习应用的进步,以改善医疗保健.
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