摘要
针对图像分类的各种深度神经网络模型发展迅速,但是在训练样本极少的情况下,深度学习模型普遍无法取得良好的样本外表现。纹理是医学影像中图像识别的关键特征,本文使用灰度共生矩阵(Gray Level Co-Occurrence Matrix, GLCM)提取胸部X光片的纹理特征,与预训练的ResNet50提取的特征进行融合,最后使用机器学习分类模型对胸部X光片进行肺炎识别。在本文的7种分类算法下,使用逻辑回归分类取得了最好的结果,该模型仅使用灰度共生矩阵提取的特征的分类准确率为80%,使用预训练的ResNet50提取的特征的分类准确率为90%,融合特征分类准确率为92.5%。实验结果显示,与仅使用一种方法提取特征的分类结果相比,融合特征再分类的方法使准确率有明显的提高。
Various deep neural network models for image classification have developed rapidly, but deep learning models generally cannot achieve good out-of-sample performance with very few training samples. Texture is a key feature of medical images in image recognition, we use GLCM to extract texture features from chest X-rays, which are fused with the features extracted from pretrained ResNet50. Finally, a machine learning classification model is used to recognize pneumonia from chest X-rays. Among the seven classification algorithms in this article, the best results were achieved using Logistic Regression. The classification accuracy of the model is 80% for features extracted using only the GLCM, 90% for features extracted using pre-trained ResNet50, and 92.5% for fused features. The experimental results show that compared to the classification results using only one method to extract features, the fusion feature re-classification method significantly improves the accuracy.
出处
《计算机科学与应用》
2024年第2期489-500,共12页
Computer Science and Application
