具有双分支网络的临床皮肤病变的分类
Hui Wang1, Qianqian Qi1, Weijia Sun1
1College of Computer Science and Engineering, Changchun University of Technology, Changchun, China.
Frontiers in medicine
|June 26, 2023
概括
一个新的双分支网络 (DBN) 通过有效关注复杂背景中的病变区域,提高了皮肤病变分类的准确性. 这种计算机辅助诊断系统在新的临床皮肤病变图像数据集上显示出有希望的结果.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 恶性皮肤病变由于与良性病变的相似性以及当前方法的局限性而构成诊断挑战.
- 现有的计算机辅助诊断系统与稀疏的数据集,复杂的背景和临床图像中的噪声作斗争.
- 目前的模型缺乏有效关注复杂背景中的损伤区域的能力.
研究的目的:
- 开发一种先进的计算机辅助诊断系统,用于恶性皮肤病变.
- 通过深度学习提高皮肤病变分类的准确性和效率.
- 解决现有模型在处理复杂的临床图像数据方面的局限性.
主要方法:
- 提出了一种新的双分支网络 (DBN),包括共同特征提取块 (CFEBlock) 和FusionBlocks.
- 通过结合现有和新收集的数据,开发了一个新的临床皮肤损伤图像 (CSLI) 数据集,包括六个类别的3361张图像.
- 采用双分支网络架构,用于增强功能提取和层间信息传输.
主要成果:
- 在测试数据上,DBN表现出强大的整体性能,使用准确度,精度,灵敏度,特异性,F1得分,平衡准确度和AUC等指标进行评估.
- 可视化,ROC曲线和混矩阵证实了网络在各种疾病类别中的有效性.
- DBN成功地提高了对具有复杂背景的受损地区的关注度.
结论:
- 拟议的DBN架构为医学图像分类提供了更高的灵活性和准确性.
- 双分支结构促进了特征转移,并为未来的模型开发提供了重大潜力.
- CSLI数据集和DBN为推进自动化皮肤病变诊断研究提供了宝贵的资源.
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