Lung cancer poses a significant global public health challenge, emphasizing the importance of early detection for improved patient outcomes. Recent advancements in deep learning algorithms have shown promising results in medical image analysis. This study aims to explore the application of object detection particularly YOLOv5, an advanced object identification system, in medical imaging for lung cancer identification. To train and evaluate the algorithm, a dataset comprising chest X-rays and corresponding annotations was obtained from Kaggle. The YOLOv5 model was employed to train an algorithm capable of detecting cancerous lung lesions. The training process involved optimizing hyperparameters and utilizing augmentation techniques to enhance the model's performance. The trained YOLOv5 model exhibited exceptional proficiency in identifying lung cancer lesions, displaying high accuracy and recall rates. It successfully pinpointed malignant areas in chest radiographs, as validated by a separate test set where it outperformed previous techniques. Additionally, the YOLOv5 model demonstrated computational efficiency, enabling real-time detection and making it suitable for integration into clinical procedures. This proposed approach holds promise in assisting radiologists in the early discovery and diagnosis of lung cancer, ultimately leading to prompt treatment and improved patient outcomes.

翻译：肺癌是全球公共卫生领域面临的重大挑战，早期检测对改善患者预后具有重要意义。近年来深度学习算法在医学图像分析领域展现出令人鼓舞的成果。本研究旨在探索目标检测技术——特别是YOLOv5这一先进目标识别系统——在肺癌医学影像识别中的应用。为训练和评估算法，我们从Kaggle平台获取了包含胸部X光片及相应标注的数据集。采用YOLOv5模型训练能够检测肺部癌性病变的算法，训练过程涉及超参数优化与数据增强技术以提升模型性能。训练后的YOLOv5模型在识别肺癌病灶方面展现出卓越能力，获得高准确率与召回率。该模型成功定位胸部X光片中的恶性区域，独立测试集验证其性能优于现有技术。此外，YOLOv5模型具备计算高效性，可实现实时检测，适合融入临床诊疗流程。本研究提出的方法有望辅助放射科医师实现肺癌早期发现与诊断，促进及时治疗并改善患者预后。