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This paper presents an approach for the classification of child chest X-ray images into two classes: pneumonia and normal. We employ Convolutional Neural Networks, from pre-trained networks together with a quantization process, using the platform TensorFlow Lite method. This reduces the processing requirement and computational cost. Results have shown accuracy up to 95.4% and 94.2% for MobileNetV1 and MobileNetV2, respectively. The resulting mobile app also presents a simple and intuitive user interface.

This paper aims to compare the behave of different signals when applied to different compression techniques, to test and find the best compression techniques to each different signal, also proving that different signals behave differently in distinct types of compression, the results of this work was satisfactory to prove that different types of compression can be used on signals to achieve better results.