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This paper describes a comparison between three pre-trained neural networks for the classification of chest X-ray images: Xception, Inception V3, and NasNetLarge. Networks were implemented using learning transfer; The database used was the chest x-ray data set, which contains a total of 5856 chest x-ray images of pediatric patients aged one to five years, with three classes: Normal Viral Pneumonia and Bacterial Pneumonia. Data were divided into three groups: validation, testing and training. A comparison was made with the work of Kermany et al. (2018) who implemented the Inception V3 network in two ways: (Pneumonia X Normal) and (Bacterial Pneumonia X Viral Pneumonia). The nets used had good accuracy, being the NasNetLarge network the best precision, which was 95.35 % (Pneumonia X Normal) and 91.79 % (Viral Pneumonia X Bacterial Pneumonia) against 92.80 % in (Pneumonia X Normal) and 90.70 % (Viral Pneumonia X Bacterial Pneumonia) from kermany’s work, the Xception network also achieved an improvement in accuracy compared to kermany’s work, with 93.59 % at (Normal X Pneumonia) and 91.03 % in (Viral Pneumonia X Bacterial Pneumonia).

Classification of x-ray images for detection of childhood pneumonia using pre-trained neural networks