Aim and Objective:
Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for
patients with lung cancer. Numerous methods based on convolutional neural networks (CNNs) have been proposed for lung nodule
detection in computed tomography (CT) images. With the collaborative development of computer hardware technology, the detection
accuracy and efficiency can still be improved.
Materials and Methods:
In this study, an automatic lung nodule detection method using CNNs with transfer learning is presented. We
first compare three of the state-of-the-art convolutional neural network (CNN) models, namely, VGG16, VGG19 and ResNet50, to
determine the most suitable model for lung nodule detection. We then utilize two different training strategies, namely, freezing layers
and fine-tuning, to illustrate the effectiveness of transfer learning. Furthermore, the hyper-parameters of the CNN model such as optimizer, batch size and epoch are optimized.
Results:
Evaluated on the Lung Nodule Analysis 2016 (LUNA16) challenge, promising results with an accuracy of 96.86%, a precision of 91.10%, a sensitivity of 90.78%, a specificity of 98.13%, and an AUC of 99.37% are achieved.
Conclusion:
Compared with other works, state-of-the-art specificity is obtained, which demonstrates that the proposed method is effective
and applicable to lung nodule detection.
Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification