Lung cancer remains a leading cause of cancer-related deaths worldwide. Early detection using chest CT scans can significantly improve patient outcomes, yet accurate diagnosis remains a challenge due to the complex morphology of lung nodules. This paper presents a modified YOLO-based deep learning framework that enhances real-time detection and classification of lung nodules. We introduce architectural changes such as the use of RepC3 modules and deeper convolutional layers in the backbone to improve feature extraction and localization. Experimental results on the LIDC-IDRI dataset show a mean Average Precision (mAP@0.5) of 77.74%, demonstrating the model's effectiveness in detecting and classifying nodules with reasonable accuracy and speed.
