Cell defect recognition based on deep learning

• Main Authors: Zhou Jiankai, Xu Shengzhi, Zhao Ergang, Yu Mei, Zhang Jianjun
• Format: Article
• Language: zho
• Published: National Computer System Engineering Research Institute of China 2019-05-01
• Series: Dianzi Jishu Yingyong
• Subjects: electrofluorescence; image recognition; TensorFlow; convolutional neural network
• Online Access: http://www.chinaaet.com/article/3000101569

Based on the TensorFlow framework, this paper builds convolutional neural networks to recognize the defects in the electroluminescent images of cells. It selects the exposed data set that contains the different types of defects in the cell. Based on the traditional VGGNet network, the full convolution neural network is used for training, and this paper analyzes the training effects of different loss functions and dropout probabilities on data set. Experiments have shown that the algorithm accurately recognizes whether the cell is defective. The study also shows that the compression network structure greatly increases the training rate of the algorithm, which makes the simplified model more portable and provides an effective solution for a wide range of real-time defect recognition.