![]() Current strategies for reducing pneumonia deaths include early detection and appropriate treatment of pneumonia. In developed countries, the etiology and clinical features of ALRI have been extensively investigated however, ALRI remains a serious cause of childhood death in developing countries with an estimated 4 million deaths annually 5. The importance of acute lower respiratory diseases is reflected not only in the morbidity and mortality rates, but also in the long-term consequences. ![]() ALRI is also the leading cause of child death worldwide, accounting for 20% of mortality in children less than 5 years old 3, 4. The high rates of hospitalization for acute lower respiratory infection (ALRI) among children have been highlighted 1, 2 The hospitalization rate for children with acute lower respiratory infection is 5772 per 100,000 1. This system can be a good diagnostic assistance under limited medical resources. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. 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. Among the three methods, the one-versus-one scheme has the best performance. Our model demonstrated a good distinguishing ability for these common lung problems in children. 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. 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. 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. We need a powerful system as a diagnostic tool for most common lung diseases in children. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Better diagnostic and therapeutic strategies are still needed in poor countries. Current strategies to reduce this problem include early detection and appropriate treatment. Acute lower respiratory infection is the leading cause of child death in developing countries.
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