Optimizations of Lung Cancer Detection and Classification based on YOLO Architecture
學年 113
學期 2
發表日期 2025-06-25
作品名稱 Optimizations of Lung Cancer Detection and Classification based on YOLO Architecture
作品名稱（其他語言）
著者 Angus Wu, Chii-Jen Chen*
作品所屬單位
出版者
會議名稱 The International Conference on Recent Advancements in Computing in AI, IoT and Computer Engineering Technology (CICET 2025)
會議地點 New Taipei, Taiwan
摘要 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.
關鍵字 Lung cancer, Lung nodule detection and classification, Early diagnosis.
語言 en_US
收錄於
會議性質 國際
校內研討會地點 淡水校園
研討會時間 20250625~20250627
通訊作者
國別 TWN
公開徵稿
出版型式
出處