[论文解读] Leveraging object detection for the identification of lung cancer

Karthick Prasad Gunasekaran|arXiv (Cornell University)|May 25, 2023

Radiomics and Machine Learning in Medical Imaging被引用 12

一句话总结

该研究将 YOLOv5 目标检测应用于胸部X光，以识别肺癌病灶，在 Kaggle 数据集上训练，达到高准确率/召回率并支持实时检测。

ABSTRACT

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.

研究动机与目标

• 促使早期检测肺癌并探索深度学习在医学影像分析中的应用。
• 研究将目标检测模型（YOLOv5）应用于识别胸部放射线中的肺部癌灶。
• 通过数据增强训练并优化超参数以提升性能。
• 在单独的测试集上评估模型并评估在临床整合中的计算效率。

提出的方法

• 在胸部 X 线图像中使用 YOLOv5 进行病灶检测。
• 在具有相应注释的 Kaggle 数据集上进行训练。
• 优化超参数并应用数据增强以提升鲁棒性。
• 在单独的测试集上评估性能并与先前技术进行比较。
• 评估计算效率以支持潜在的实时部署。

实验结果

研究问题

• RQ1 YOLOv5 是否能够在胸部放射影像中准确检测恶性肺部病变？
• RQ2 在保留的测试集上，模型的准确率和召回率是多少？
• RQ3 该方法的计算效率是否足以用于实时或近实时的临床使用？
• RQ4 基于 YOLOv5 的方法与该领域的早期技术相比如何？

主要发现

• YOLOv5 模型在识别肺癌病灶方面表现出高准确率和召回率。
• 它成功定位了胸部 X 光中的恶性区域。
• 在单独的测试集上验证了性能，并优于先前的技术。
• 该模型展示了适合实时检测的计算效率。
• 该方法有望帮助放射科医生在早期发现和诊断中发挥作用。