A Novel Mask R-CNN based generated mask approach for the detection and classification of lung cancer in CT images

Background: The integration of computer vision systems has the potential to revolutionize various medical disciplines, particularly those reliant on diagnostic imaging. These systems are instrumental in diagnosing numerous medical conditions, including fractured bones, lung cancer, and heart diseases. Lung cancer, which ranks among the top four causes of mortality worldwide, presents significant challenges in accurate diagnosis. The complexity of lung regions in computed tomography (CT) images often necessitates manual delineation, which is time-consuming and prone to variability. Automated methods for lung segmentation can enhance clinical workflows and improve diagnostic accuracy, addressing this critical gap.