Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks
Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks
复制标题
基于卷积深度神经学习网络利用胸部 X 光图像预测肺炎
DOI：
10.3233/jifs-191438
复制
发表时间：
2020-01-01
影响因子：
2
通讯作者：
Cheng, Ming
中科院分区：
文献类型：
--
作者：
Wu, Huaiguang;Xie, Pengjie;Cheng, Ming
胸部X线检查是筛查和诊断多种肺部疾病的重要手段之一。通过胸部X线诊断肺炎是医学专家常用的方法之一。然而，胸片图像质量存在对比度低、脏器重叠、边界模糊等缺陷，严重影响了肺炎的检出。因此，通过大量的胸部X线图像构建稳定、准确的肺炎自动检测模型具有重要的医学价值和应用意义。在本文中，我们提出了一种新的混合系统检测肺炎胸部X射线图像：ACNN-RF，这是一个自适应中值滤波卷积神经网络（CNN）识别模型的基础上随机森林（RF）。首先，采用改进的自适应中值滤波算法对胸片图像进行去噪，使图像更易于识别。其次，我们建立了基于Dropout的CNN架构，从每张胸部X射线图像中提取深度激活特征。最后，我们采用基于GridSearchCV类的RF分类器作为CNN模型中深度激活特征的分类器。它不仅避免了数据训练中的过拟合现象，而且提高了图像分类的准确率。在我们的实验中，实验中使用的公共胸部X射线图像数据集包含5863张图像，其中包括1574名独特患者的4265张正面视图X射线图像。肺炎的平均识别率高达97%的建议ACNN-RF。实验结果表明，ACNN-RF识别系统比传统的图像识别系统更有效。
The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.