Evaluation of medical X-ray images are traditionally carried out by visual inspection by medics, however some factors such as similar radiological findings, image quality, overlapping anatomical structure and subjectivity in interpretation make correct diagnosis difficult and may even delay treatments due to the need for a second medical opinion. The application of machine learning (ML) algorithms (MLA) to X-ray images can increase the accuracy and agility in disease diagnosis and assist doctors in decision-making. The objective of this pilot study is to test three deep learning (DL) architectures, including VGG16 and EfficientNet convolutional neural networks (CNN), evaluating their classification ability applied to chest X-rays, with and without lung pathologies and explore the use of Gradient-weighted Class Activation Mapping (Grad-CAM) as a tool for neural network interpretability. Images were taken from Kaggle datasets, and we used undersampling to prevent biases in training. The results demonstrate high accuracy in disease classification, particularly with EfficientNet. Grad-CAM was employed for interpretability of the trained CNN model, which produces a heatmap, highlighting the most important image regions for classification decisions. Despite promising results, analysis using Grad-CAM revealed room for improvement, prompting the need for collaboration with radiologists to evaluate the accuracy and clinical utility of our methodology. Future work may involve a hybrid approach, combining undersampling and oversampling techniques, using data augmentation or a larger variation of images, as well as the exploration of generative adversarial networks for superior results. Collaboration with the medical community is important for applying ML to medical imaging and analyzing tools for interpretability.