Deep Learning Approaches for Segmentation of Lung Nodules in CT Scans

Recent developments in medical imaging have demonstrated the great potential of deep learning techniques to improve the precision and effectiveness of CT scans used for segmenting lung nodules. This study uses two popular datasets, LIDC-IDRI and LUNA16, to compare deep learning methods with more conventional approaches. The datasets have not been officially named yet. The Sørensen-Dice Coefficient (DSC), an important measure for evaluating segmentation accuracy, is the main focus of the examination. According to our results, deep learning approaches are much more effective than the conventional ones. In particular, deep learning attained a DSC of 0.853 on the first dataset, likely LIDC-IDRI, while conventional approaches only managed a DSC of 0.761. Similarly, deep learning methods achieved a DSC of 0.763 on the second dataset, which is probably LUNA16, higher than the 0.704 achieved by conventional methods. These outcomes demonstrate the efficacy of deep learning in tackling the intricacies and variations of lung nodule segmentation.