Title:Pulmonary Nodule Detection Using RBB-Based Optimised Yolov8x-C2f Network
Volume: 18
Author(s): Swati Chauhan*, Nidhi Malik and Rekha Vig
Affiliation:
- Department of Computer Science & Engineering, The North Cap University, Gurugram, Haryana, India
Keywords:
Lung nodules, CT images, deep learning, CNN, YOLO, YOLOv8x.
Abstract:
Introduction: 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.
