College of Computer Science, Xi'an University of Science and Technology, Shanxi 710054, China.
Comput Math Methods Med. 2022 Feb 22;2022:1558607. doi: 10.1155/2022/1558607. eCollection 2022.
Since manual hemolysis test methods are given priority with practical experience and its cost is high, the characteristics of hemolysis images are studied. A hemolysis image detection method based on generative adversarial networks (GANs) and convolutional neural networks (CNNs) with extreme learning machine (ELM) is proposed. First, the image enhancement and data enhancement are performed on a sample set, and GAN is used to expand the sample data volume. Second, CNN is used to extract the feature vectors of the processed images and label eigenvectors with one-hot encoding. Third, the feature matrix is input to the map in the ELM network to minimize the error and obtain the optimal weight by training. Finally, the image to be detected is input to the trained model, and the image with the greatest probability is selected as the final category. Through model comparison experiments, the results show that the hemolysis image detection method based on the GAN-CNN-ELM model is better than GAN-CNN, GAN-ELM, GAN-ELM-L1, GAN-SVM, GAN-CNN-SVM, and CNN-ELM in accuracy and speed, and the accuracy rate is 98.91%.
由于手动溶血试验方法具有实践经验且成本较高，因此研究了溶血图像的特征。提出了一种基于生成对抗网络（GAN）和卷积神经网络（CNN）的溶血图像检测方法，该方法结合了极限学习机（ELM）。首先，对样本集进行图像增强和数据增强，然后使用 GAN 扩展样本数据量。其次，使用 CNN 提取处理图像的特征向量，并使用独热编码标记特征向量。第三，将特征矩阵输入到 ELM 网络的映射中，通过训练最小化误差并获得最优权重。最后，将待检测的图像输入到训练好的模型中，选择概率最大的图像作为最终类别。通过模型对比实验，结果表明，基于 GAN-CNN-ELM 模型的溶血图像检测方法在准确性和速度方面优于 GAN-CNN、GAN-ELM、GAN-ELM-L1、GAN-SVM、GAN-CNN-SVM 和 CNN-ELM，准确率达到 98.91%。
