Lung cancer is the third most common cancer and the leading cause of cancer-related death in America. This cancer has a high lethality with an overall survival of 16% at five years. Symptoms are nonspecific, so diagnosis is usually delayed. To achieve earlier diagnosis and initiate treatment at a non-advanced stage of the cancer to reduce mortality, low-dose computed tomography (CT) scans are performed. Therefore, advanced image processing and machine learning techniques are required since the high volume of images generated by medical equipment causes the review of a lot of information to make a medical diagnosis. For diagnosis, the images are analyzed by specialists in order to find nodules, measure them and evaluate them. However, the nodules found in the lungs have different shapes, dimensions and textures, which makes identification difficult. For this reason, this paper presents the implementation, analysis and evaluation of two deep learning techniques for the detection of pulmonary nodules in CT scans, resulting in prediction models with a high percentage of accuracy.
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.
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