NoduleNet: A Lung Nodule Classification Using Deep Learning

Background: Accurate detection and classification of lung nodules at an early stage can help physicians to improve the treatment outcomes of lung cancer. Several lung nodule classifications using deep learning have been proposed but they are lag of external validation to Thai patient data. Objective: To propose a deep learning model called NoduleNet for lung nodule classification and perform internal and external validation of the proposed model. Methods: Two datasets were performed; internal validation using LUNA16 (the public lung CT database), and external validation using ChestRama (37 chest CT scans retrospectively identified from the CT database of Ramathibodi Hospital between 2017 and 2019). The NoduleNet was built on top of pretrained architecture, VGG16, and VGG19 with customization. Results: The NoduleNet showed impressive results in nodule classification. The best model achieved accuracy of 0.95 (0.94 - 0.96), sensitivity of 0.84 (0.82 - 0.86), and specificity of 0.97 (0.97 - 0.98) for internal validation, where the external validation results was accuracy of 0.95 (0.87 - 1.00), sensitivity of 0.91 (0.82 - 1.00), and specificity of 1.00 (1.00 - 1.00). There were 3 misclassified samples in external validation which are all false-negative. Conclusions: The NoduleNet is able to generalize from non-Thai patient data to Thai patient data. It could be further improved by taking sequence of images into account, integrating with an automatic nodule detection algorithm, and adding more nodule types.