Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks

Abstract

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.