基于MSCDNet的皮肤癌的多类分类,使用皮肤镜图像进行分类
Vankayalapati Radhika1, B Sai Chandana1
1School of Computer Science Engineering, VIT-AP University, Amaravathi, India.
PeerJ. Computer science
|September 14, 2023
概括
早期发现皮肤癌对于恢复至关重要. 这项研究引入了一种先进的深度学习模型,MSCD-Net,用于准确的多类皮肤癌分类,提高诊断效率.
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:
- 皮肤癌对健康构成重大风险,早期发现可大大改善患者的治疗结果.
- 深度学习模型越来越多地用于皮肤癌检测.
- 这项研究的重点是分类多种皮肤癌类型,包括黑色素瘤,基底细胞癌,黑色素细胞瘤和良性角质瘤.
研究的目的:
- 引入一种基于深度学习的新型网络模型,用于自动分类多种皮肤癌类型.
- 为皮肤病变开发一个高效的语义细分模型.
- 提高皮肤癌诊断的准确性和效率.
主要方法:
- 开发了一个多类皮肤癌检测网络 (MSCD-Net).
- 为皮肤病变细分提出了一个高效的语义细分深度学习模型,DenseUNet.
- 使用二进制龙算法 (BDA) 进行特征选择,然后使用基于SqueezeNet的分类.
主要成果:
- 通过利用DenseNet连接和UNet架构,DenseUNet模型通过使用低级别的功能实现了优异的细分结果.
- 拟议的MSCD-Net模型在ISIC 2019数据集上与以前的方法相比,显示出更高的有效性和效率.
- 通过使用标准ISIC 2019数据集严格评估绩效.
结论:
- 开发的MSCD-Net模型在自动化多类皮肤癌检测方面提供了有希望的进步.
- 集成DenseUNet进行细分和BDA进行特征选择,有助于提高诊断准确度.
- 这些发现表明,这种深度学习方法可以显著帮助早期和有效地识别各种皮肤癌症.
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