Deep Learning for the Classification of Lung Nodules
This work addresses lung nodule classification for medical imaging, but it appears incremental as it applies an existing deep learning method to a specific domain without major methodological breakthroughs.
The study tackled lung nodule classification in thoracic CT images using a deep convolutional neural network, achieving classification accuracy, and found that simplistic geometric nodules fail to capture important features.
Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.