Using Deep Learning Techniques to Evaluate Lung Cancer Using CT Images

Lung cancer is the most frequent cancer globally. New technologies have recently piqued the interest of the healthcare world due to their ability to automate or provide additional information to medical personnel. After lung cancer has been diagnosed, it is compared with nearby areas on CT scans in order to determine how far the disease has spread. This work aimed to identify characteristics of tumorous lungs on CT scans utilizing new machine learning technologies. Although 3D ResNet architecture could learn opacity and cancer (AUC = 0.61), it was not better than chance. As a result, only emphysema was learned, attaining an AUC of 0.79. The network was then added with a transfer learning approach to improve results. Finally, a self-supervision transfer learning approach and training without prior knowledge were contrasted. The transfer learning method produced comparable results in the multi-task approach for emphysema (AUC = 0.78 versus 0.60 without pre-training) and opacities (AUC = 0.61). To use this as intended, the classification can be used to anticipate future health complications which may occur if cancer has spread to other parts of the body.