Pulmonary Nodule Detection Using RBB-Based Optimised Yolov8x-C2f Network

Background: Malignant lung nodules are a major cause of lung cancer-related mortality and rank among the most prevalent cancers worldwide. Due to a lack of contrast, little nodules blend with their surroundings and other structures; therefore, it is difficult to detect them efficiently during the diagnostic phase, making it challenging for the radiologist to determine whether the nodule is malignant or not. This study evaluates the model’s performance in rapidly and accurately detecting nodules from lung CT scans Methods: Nodule detection is accomplished by using conventional diagnostic procedures such as radiographic imaging techniques and computerized tomography (CT). However, these methods aren't always effective in spotting tiny nodules, and they can put patients at risk of radiation exposure. Consequently, there has been a lot of research in this field using deep learning to process images and identify nodules in the lungs. To improve the model's accuracy, our method employs a residual bounding box-based optimized “You Only Look Once version 8x-Coordinates-To- Features” (YOLOv8x-C2f) model in conjunction with a handful of preprocessing steps. Results: This model is evaluated with the help of the “Lung Image Database Consortium and Image Database Resource Initiative” LIDC/IDRI dataset, which was acquired through the lung nodule analysis (LUNA16) grand challenge. With an impressive mean average precision (mAP50) of 0.70% and precision of 89%, the suggested model achieves an impressive accuracy of 95.2% when it comes to nodule recognition with a confidence factor. Conclusion: The study demonstrates that the model's superior architecture and features can accurately identify and localize nodules, enhancing overall performance relative to state-of-the-art approaches.