Dataset Information

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

ABSTRACT: Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.

SUBMITTER: Chen KC

PROVIDER: S-EPMC7566516 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

Chen Kai-Chi KC Yu Hong-Ren HR Chen Wei-Shiang WS Lin Wei-Che WC Lee Yi-Chen YC Chen Hung-Hsun HH Jiang Jyun-Hong JH Su Ting-Yi TY Tsai Chang-Ku CK Tsai Ti-An TA Tsai Chih-Min CM Lu Henry Horng-Shing HH

Scientific reports 20201015 1

Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung diseas ...[more]

PMID: 33060702

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Dataset Information

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

Publications

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

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