DeepHistoNet:一个强大的深度学习模型用于肝细胞,肺和结肠癌的分类
Ravindranath Kadirappa1, Deivalakshmi S1, Pandeeswari R1
1Department of ECE, National Institute of Technology, Tiruchirappalli, India.
Microscopy research and technique
|September 26, 2023
概括
一个新的深度学习模型,DeepHistoNet,可以从组织病理图像中准确检测癌症. 这种计算机辅助诊断系统显著减少了手工工作和时间,为早期癌症识别提供了高准确度.
科学领域:
- 在瘤学瘤学.
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 癌症等非传染性疾病 (NCD) 需要先进的诊断工具.
- 早期癌症检测对于有效治疗和改善患者治疗结果至关重要.
- 手动癌症诊断是耗时和劳动密集的.
研究的目的:
- 为非传染性疾病开发计算机辅助诊断系统.
- 创建一个深度学习 (DL) 模型,从组织病理图像中准确识别癌症.
- 提高癌症诊断的效率和减少癌症诊断的时间复杂性.
主要方法:
- 使用卷积神经网络开发了一个深度学习模型DeepHistoNet.
- 该模型结合了以注意力为导向的,密集连接的残余和扩展的卷积技术.
- 在KMC,TCGA-LIHC和LC25000数据集上进行了实验.
主要成果:
- 在KMC肝脏数据集上,DeepHistoNet获得了97.1%的准确性和AUC-ROC为0.9867的结果.
- 与最先进的方法相比,该模型表现出更高的性能.
- 在LC25000数据集上,DeepHistoNet获得了99.8%的分类准确度.
结论:
- DeepHistoNet 是一种新且强大的深度学习方法,用于基因病学图像分类.
- 该模型有效地提取精确的模式,用于准确的癌症分类.
- 拟议的方法为早期癌症检测和诊断提供了一个有希望的解决方案.
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