[1]龚 安,郭文婷.基于卷积神经网络的皮肤癌识别方法[J].计算机技术与发展,2020,30(10):167-172.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 030]
　GONG An,GUO Wen-ting.Skin Cancer Image Classification Method Based on Convolutional Neural Network[J].,2020,30(10):167-172.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 030]
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基于卷积神经网络的皮肤癌识别方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年10期
页码:
167-172
栏目:
应用开发研究
出版日期:
2020-10-10

文章信息/Info

Title:
Skin Cancer Image Classification Method Based on Convolutional Neural Network
文章编号:
1673-629X(2020)10-0167-06
作者:
龚 安; 郭文婷
中国石油大学(华东) 计算机科学与技术学院,山东 青岛 266580
Author(s):
GONG An; GUO Wen-ting
School of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China
关键词:
皮肤癌识别; 卷积神经网络; 迁移学习; 数据增强; 特征融合
Keywords:
skin cancer recognition; convolutional neural network; transfer learning; data augmentation; feature fusion
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 10. 030
摘要:
为了解决色素性皮肤病类间相似度高、类内差异化大的特点导致的皮肤癌识别工作中的误判、准确率低等问题,在迁移学习的基础上, 提出了一种基于卷积神经网络特征融合识别皮肤癌的方法。首先,为了防止出现数据不平衡以及样本小带来的过拟合问题,进行数据增强。然后将数据集分别在预训练后的 DenseNet 模型以及 Xception 模型进行训练,得到的特征进行融合,交叉利用特征信息,循环采用上次保留的最佳权重作为模型权重进行训练,进而实现皮肤癌图像的识别。 实验结果表明,该方法的准确率和敏感性可分别达到 91.42%、87.37% ,相比未进行特征融合的模型, 准确率和敏感性均有所提高, 有效地解决了皮肤癌类间相似度高, 类内差异大的问题,进而有效地改善临床医学诊断效率的问题。
Abstract:
In order to solve the problem like misjudgment and low accuracy in the recognition of skin cancer caused by the high similarity between diff-erent classes and the large differences within the same classes in the pigmented skin diseases,on the basis of transfer learning,a method based on convolutional neural network feature fusion to identify skin cancer is proposed. Firstly, in order to prevent data imbalance and over-fitting caused by small samples,data augmentation is performed. Then,the dataset is trained in the pre-trained DenseNet model and the Xception model respectively, and the obtained features are fused. The feature information is cross-utilized,and the optimal weight retained last time is cycli-cally used as the model weight for training,so as to realize the recognition of skin cancer images. The experiment shows that the accuracy and sensitivity of the proposed method can reach 91.42% and 87.37% respectively. Compared with the model without feature fusion,the accuracy and sensitivity are improved,which can effectively solve the problem of high similarity between skin cancers and great difference within skin cancers,thus effectively improving the efficiency of clinical medical diagnosis.

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更新日期/Last Update: 2020-10-10