Angxuan Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Angxuan Chen has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Liyang Wang
School of Clinical Medicine, Tsinghua University, Beijing 100084, China
1 shared publication - Qun Shen
National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China; National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, PR China
1 shared publication - Xiaoya Wang
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
1 shared publication Yan Zhang
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
1 shared publication- Yong Xue
National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China; National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, PR China
1 shared publication - Yu Zhang
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
- 13 April 2020
- Published by MDPI AG in Diagnostics
- Vol. 10 (4) , 217
AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks
Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.
- 13 April 2020
- Published by MDPI AG in Diagnostics
- Vol. 10 (4) , 217
Open AccessAK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks
Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.