Artificial intelligence (AI) arises from the desire of human beings to
reproduce their intelligent behavior by means of computers. Recently, AI has been
demonstrating remarkable success in medical image analysis owing to the rapid
progress of deep learning algorithms, which have shown increasing power to solve
complex real-world problems in computer vison and image analysis. The aim of this
work is to introduce the main AI techniques for an interdisciplinary reader profile,
specifically deep learning approaches applied nowadays in Radiology, for the
intelligent diagnosis of lung cancer and respiratory diseases such as viral and bacterial
pneumonia, tuberculosis and Covid-19. An overview of current techniques is presented,
framed mainly in the area of deep learning and in particular convolutional neural
networks. Reference to current reviews on techniques, applications, needs, software
and databases is made. Then, the Deep Learning paradigm is introduced from its
origins to then focus on CNNs, their main characteristics, aspects to be considered in
training, representative architectures, implementation issues, including transfer
learning. Finally, the software and hardware platforms widely used for the problem are
described and then the concluding remarks are presented.
Keywords: Artificial Intelligence, Chest X-Ray, CNN, COVID-19, CT image, Deep Learning, Diagnosis, Pneumonia, Transfer learning, Tuberculosis.
