...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The Journal of investigative dermatology. >Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm
【24h】

Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

机译：使用深层学习算法对良性和恶性皮肤肿瘤进行临床图像的分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 +/- 0.01, 0.83 +/- 0.01, 0.82 +/- 0.02, and 0.96 +/- 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 +/- 0.01, 0.91 +/- 0.01, 0.83 +/- 0.01, and 0.88 +/- 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% +/- 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.
机译：我们测试了使用深度学习算法的使用来分类12个皮肤病的临床图像 - 基础细胞癌，鳞状细胞癌，上皮内癌，光化角膜病，脂肪虫癌，恶性黑素瘤，黑素状痣，熊葡萄球菌，化脓性肉芽肿，血管瘤，皮肤病瘤和疣。卷积神经网络（Microsoft Reset-152型号; Microsoft Research Asia，北京，中国）用Asan DataSet，MED-Node DataSet的训练部分和Atlas网站图像（总共19,398张图像）进行微调。训练有素的模型与Asan，Hallym和爱丁堡数据集的测试部分进行了验证。随着ASAN数据集，曲线下的面积为基础细胞癌，鳞状细胞癌，上皮内癌和黑素瘤的曲线为0.96 +/- 0.01,0.83 +/- 0.01,0.82 +/- 0.02和0.96 + / - 0.00分别。与爱丁堡数据集，相应疾病曲线下的面积分别为0.90 +/- 0.01,0.91 +/- 0.01,0.83 +/- 0.01和0.88 +/- 0.01。利用HALLIM数据集，基底细胞癌诊断的敏感性为87.1％+/- 6.0％。具有480个ASAN和爱丁堡图像的测试算法性能与16位皮肤科医生的性能相当。为了提高卷积神经网络的表现，应收集具有更广泛的年龄和种族的额外图像。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号