针对现有方法在肺结节检测中准确率低及存在过拟合现象的问题,提出一种基于改进YOLACT模型的肺结节检测方法。在模型的主体结构上,采用DetNet替代原始的残差网络,解决了原始模型在小型结节检测上的局限性。在模型训练上,针对原模型在少量肺结节数据上学习困难而引起的过拟合问题,引入迁移学习机制,帮助新模型得到更好的检测结果。使用RReLU激活函数代替原有的ReLU激活函数,减少了原模型可能存在的过拟合现象。在LUNA16数据集上的实验结果表明,所提方法在受试者工作曲线下面积、假阳率、漏诊率及准确率上均取得了一定的提升。

To address the limitations of existing detection methods in pulmonary nodule detection, such as low accuracy and over-fitting, a pulmonary nodule detection method based on an improved YOLACT model was proposed. In the main structure of the YOLACT model, the original residual network was replaced with DetNet to overcome the limitation of the original model in small nodule detection. Further, a transfer-learning mechanism was introduced in the model training to prevent the over-fitting problem of the original model induced by learning difficulties on a small number of pulmonary nodules, thereby allowing the new model to achieve better detection results. Moreover, the original ReLU function was replaced with the RReLU function to further reduce the possibility of over-fitting. Experimental results on LUNA16 dataset indicate that the proposed method can achieve improvement under several evaluation metrics, such as the working curve of the subject, rate of false positives, rate of missed diagnosis, and accuracy.