TY  - EJOU
AU  - Zhang, Yang 
AU  - Qiu, Liru 
AU  - Zhu, Yongkai 
AU  - Wen, Long 
AU  - Luo, Xiaoping 

TI  - A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2022
VL  - 133
IS  - 3
SN  - 1526-1506

AB  - Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4 algorithm is trained to detect and position the chest part in the raw images. Secondly, a novel attention network is proposed, named SGNet, which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal images. Thirdly, the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of FG-CPD. The FG-CPD has been tested on the public Chest X-ray 2017 dataset, and the results show that it has achieved great effect. Then, the FG-CPD is tested on the real chest X-ray images from children aged 3–12 years ago from Tongji Hospital. The results show that FG-CPD has achieved up to 96.91% accuracy, which can validate the potential of the FG-CPD.
KW  - Childhood pneumonia diagnosis; fine-grained classification; YOLOv4; attention network; Convolutional Neural Network (CNN)

DO  - 10.32604/cmes.2022.022322