Gaobo Liang1, Lixin Zheng2. 1. Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, College of Engineering, Huaqiao University, Quanzhou, China. 2. Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, College of Engineering, Huaqiao University, Quanzhou, China. Electronic address: zlx@hqu.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: Computer aided diagnosis systems based on deep learning and medical imaging is increasingly becoming research hotspots. At the moment, the classical convolutional neural network generates classification results by hierarchically abstracting the original image. These abstract features are less sensitive to the position and orientation of the object, and this lack of spatial information limits the further improvement of image classification accuracy. Therefore, how to develop a suitable neural network framework and training strategy in practical clinical applications to avoid this problem is a topic that researchers need to continue to explore. METHODS: We propose a deep learning framework that combines residual thought and dilated convolution to diagnose and detect childhood pneumonia. Specifically, based on an understanding of the nature of the child pneumonia image classification task, the proposed method uses the residual structure to overcome the over-fitting and the degradation problems of the depth model, and utilizes dilated convolution to overcome the problem of loss of feature space information caused by the increment in depth of the model. Furthermore, in order to overcome the problem of difficulty in training model due to insufficient data and the negative impact of the introduction of structured noise on the performance of the model, we use the model parameters learned on large-scale datasets in the same field to initialize our model through transfer learning. RESULTS: Our proposed method has been evaluated for extracting texture features associated with pneumonia and for accurately identifying the performance of areas of the image that best indicate pneumonia. The experimental results of the test dataset show that the recall rate of the method on children pneumonia classification task is 96.7%, and the f1-score is 92.7%. Compared with the prior art methods, this approach can effectively solve the problem of low image resolution and partial occlusion of the inflammatory area in children chest X-ray images. CONCLUSIONS: The novel framework focuses on the application of advanced classification that directly performs lesion characterization, and has high reliability in the classification task of children pneumonia.
BACKGROUND AND OBJECTIVE: Computer aided diagnosis systems based on deep learning and medical imaging is increasingly becoming research hotspots. At the moment, the classical convolutional neural network generates classification results by hierarchically abstracting the original image. These abstract features are less sensitive to the position and orientation of the object, and this lack of spatial information limits the further improvement of image classification accuracy. Therefore, how to develop a suitable neural network framework and training strategy in practical clinical applications to avoid this problem is a topic that researchers need to continue to explore. METHODS: We propose a deep learning framework that combines residual thought and dilated convolution to diagnose and detect childhood pneumonia. Specifically, based on an understanding of the nature of the childpneumonia image classification task, the proposed method uses the residual structure to overcome the over-fitting and the degradation problems of the depth model, and utilizes dilated convolution to overcome the problem of loss of feature space information caused by the increment in depth of the model. Furthermore, in order to overcome the problem of difficulty in training model due to insufficient data and the negative impact of the introduction of structured noise on the performance of the model, we use the model parameters learned on large-scale datasets in the same field to initialize our model through transfer learning. RESULTS: Our proposed method has been evaluated for extracting texture features associated with pneumonia and for accurately identifying the performance of areas of the image that best indicate pneumonia. The experimental results of the test dataset show that the recall rate of the method on childrenpneumonia classification task is 96.7%, and the f1-score is 92.7%. Compared with the prior art methods, this approach can effectively solve the problem of low image resolution and partial occlusion of the inflammatory area in children chest X-ray images. CONCLUSIONS: The novel framework focuses on the application of advanced classification that directly performs lesion characterization, and has high reliability in the classification task of childrenpneumonia.