Differentiation between paediatric pneumonia and normal chest X-ray images using convolutional neural networks and pseudo-attention module

The Chest X-Ray imaging as a low resource diagnosing tool that can bring sufficiently information from the thorax, helping to a specialist to find patterns with purpose to diagnose the pneumonia disease. Also, due to the simplicity to obtain these images, Chest X-Ray is the top choice against CT, US, CT, or MRI imaging in paediatric patients. In this work, we propose a novel Pseudo-attention module based on handcraft features. Generating the Region of Interest (ROI) image of the thorax, avoiding the rest of the body and eliminating the labels contained in this type of test. After obtaining the ROI image, it is evaluated with several architectures based on Convolutional Neural Networks such as DenseNET, ResNET and MobileNET. Finally, the designed system employs Grad-Cam algorithm to provide the perceptual image of the relevant features significant in the classification of Pneumonia against Normal class. The system has demonstrated similar or better performance in comparison with the state-of-the-art methods using evaluation metrics such as Accuracy, Precision, Sensibility, and F1 score.