A Cascade Model to Detect and Segment Lung Nodule Using YOLOv8 and Resnet50U‐Net

ABSTRACT In our research, we introduce a sophisticated “two‐stage” or cascade model designed to enhance the precision of lung nodule analysis. This innovative approach integrates two crucial processes: detection and segmentation. In the initial stage, a specialized object detection algorithm efficiently scans medical images to identify potential areas of interest, specifically focusing on lung nodules. This plays a crucial role in minimizing the segmentation area, particularly in the context of lung imaging, where the structures exhibit heterogeneity. This algorithm helps focus the segmentation process only on the relevant areas, reducing unnecessary computation and potential errors. Subsequently, the second stage employs advanced segmentation algorithms to precisely delineate the boundaries of the identified nodules, providing detailed and accurate contours. The combination of object detection and segmentation not only enhances the overall accuracy of lung cancer detection but also minimizes false positives, streamlines the workflow for radiologists, and provides a more comprehensive understanding of potential abnormalities. Additionally, it improves the efficiency and accuracy of segmentation, especially in cases where the complexity and heterogeneity of the lung structure make the segmentation task more challenging. This proposed method has been tested on the LIDC‐IDRI dataset, demonstrating favorable results in both nodule detection and segmentation steps, with 81.3% mAP and 83.54% DSC, respectively. These results serve as evidence that the proposed method effectively improves the accuracy of lung nodule detection and segmentation.