Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy

Lung cancer is one of the most harmful malignant tumors to human health. The accurate judgment of the pathological type of lung cancer is vital for treatment. Traditionally, the pathological type of lung cancer requires a histopathological examination to determine, which is invasive and time consuming. In this work, a novel residual neural network is proposed to identify the pathological type of lung cancer via CT images. Due to the low amount of CT images in practice, we explored a medical-to-medical transfer learning strategy. Specifically, a residual neural network is pre-trained on public medical images dataset luna16, and then fine-tuned on our intellectual property lung cancer dataset collected in Shandong Provincial Hospital. Data experiments show that our method achieves 85.71% accuracy in identifying pathological types of lung cancer from CT images and outperforming other models trained with 2054 labels. Our method performs better than AlexNet, VGG16 and DenseNet, which provides an efficient, non-invasive detection tool for pathological diagnosis.

肺癌是对人类健康危害最大的恶性肿瘤之一。肺癌病理类型的准确判断对治疗至关重要。传统上，肺癌的病理类型需要通过组织病理学检查来确定，这种方法具有侵入性且耗时。在这项工作中，提出了一种新的残差神经网络，通过CT图像识别肺癌的病理类型。由于实际中CT图像数量较少，我们探索了一种医学到医学的迁移学习策略。具体来说，在公共医学图像数据集luna16上对残差神经网络进行预训练，然后在山东省立医院收集的自有肺癌数据集上进行微调。数据实验表明，我们的方法从CT图像中识别肺癌病理类型的准确率达到85.71%，优于使用2054个标签训练的其他模型。我们的方法比AlexNet、VGG16和DenseNet性能更好，为病理诊断提供了一种高效、非侵入性的检测工具。