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Pneumonia is an inflammatory condition of the lung that affects the alveoli. Diagnosis is based on symptoms and physical examination. Chest radiographs are used as an alternative to validate the diagnosis. In the present work, a methodology is presented to perform image processing based on machine learning and artificial intelligence to perform an automatic classification of said images. Results of experiments carried out in two classification scenarios are presented: cross-validation and training and test sets. Five different machine learning methods were used in each classification scenario, as well as five evaluation metrics. Similarly, the images were preprocessed with five filters, in addition to the original images. The oriented gradient histogram feature descriptor was used to measure the effectiveness in both cases: original and with filters. The configuration of the experiment was planned in such a way that it allowed to identify the best classification conditions, also allowing to clearly observe the impact of the size of the training set on the evaluation metrics used. The results obtained allow us to see the effectiveness of the implemented methodology, since the results are competitive with those reported in the state of the art. Keywords: machine learning, artificial intelligence, neural networks, image processing.

The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.

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