An enhanced UNet variant for Effective Lung Cancer Detection

Lung cancer is one of the diseases with the highest mortality rate and early detection is key to making the disease as treatable as possible. The most common and useful method for recognizing pulmonary nodules is computed tomography, which allows them to be located and monitored. The disadvantage of this technique is that the scans have to be interpreted by doctors, who could make mistakes. The use of pulmonary CAD is therefore becoming increasingly widespread, a system capable of automatically analyzing CT images and providing information on possible suspicious regions found in the images. These systems, by offering radiologists a list of already marked regions of interest to view with particular attention, increase the efficiency of detection of small nodules and reduce reporting times by physicians. This study aims to accurately detect the location of pulmonary nodules through a Deep Learning approach with the use of computed tomography scans. In particular, it proposes the use of a new variant of the UNet architecture, called GUNet3++, which has been compared with the other types of this network. To validate the approach, the public LIDC-IDRI dataset was used, which collects pulmonary CT images of about a thousand patients with different types of cancer. The results obtained are very promising, showing a performance improvement compared to other UNet networks.