Deep Learning-based approach to classify pneumonia on chest radiographs

Abstract The diagnosis of pneumonia by X-rays is a widespread practice in the early detection of the disease and initiation of timely treatment. Automatic classification models using convolutional neural networks (CNN) have proven to be an effective and accurate tool in the diagnosis of this disease. A model that detects pneumonia in a set of chest X-rays, using two CNNs: ResNet50 & VGG16 is proposed in this work. The method consists of preprocessing and classifying the set of radiographs available in the Kaggle repository that contains 2,224 records classified by an expert and segmented into two divisions: training set (1,600 images with 800 normal and 800 pneumonia) and test set (624 images with 234 normal and 390 pneumonia). This model achieves 97% accuracy by classifying the set of chest images into two categories: normal and pneumonia. The performance of the automatic classification scenario is robust and proves to be effective in the classification of chest X-rays to detect pneumonia.