Lung cancer is the most common cause of death worldwide. So, early detection is crucial which may be helpful to reduce mortality rate. Low-dose computed tomography (LDCT) screening may reduce lung cancer-associated mortality by 20%. However the potential harm due to radiation exposure, false-positive results, and costs associated with LDCT, most organizations only recommend annual screening that targets high-risk individuals. However, due to the potential harm associated with false-positive results, the cost-effectiveness of implementing annual LDCT screening remains controversial. In order to overcome this problem, multiple research groups have attempted to develop risk prediction models to classify patients who might benefit from LDCT screening.
In this scenario, the use of artificial intelligence (AI) has resulted in good performance in predicting image-related tasks, specifically the use of convolutional neural networks (CNNs). In lung cancer research, CNNs have been applied to LDCT and chest radiographic images to facilitate detection.
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