Resumen

According to The Global Cancer Observatory (GCO), lung cancer is the type of cancer with the highest mortality rate in the world, being the most common in men and the second most frequent in women. The main factor for its high mortality is usually due to late diagnosis. Therefore, early diagnosis could help to decrease its mortality rate by applying advanced imaging techniques. It has been found that computed tomography images can be used for its diagnosis. However, the nodules that allow recognizing this kind of cancer are not easy to identify, being a difficult task for the specialist. For this reason, academic challenges have recently been proposed for researchers, where a base of images annotated by radiologists is provided in order to develop more efficient methods based on deep learning that allow the detection of these nodules. In this work, two databases acquired in the LUNA and LNDb challenges are used to perform a statistical analysis of the exams, their characteristics and the clinical diagnosis of the specialists, finding that the clinical diagnosis presents important differences between them, which makes the task of labeling the samples difficult. This analysis is useful for the development of new proposals and conclusions for the use and exploitation of deep learning in the diagnosis through medical images.