Deep Learning for the Classification of Lung Nodules

TL;DR

This paper develops a deep convolutional neural network to classify lung nodules in thoracic CT images, demonstrating the importance of complex features over simplistic geometric models for accurate diagnosis.

Contribution

Introduces a CNN architecture for lung nodule classification and highlights the limitations of geometric models in capturing nodule features.

Findings

CNN achieves high classification accuracy on lung nodule images.

Simplistic geometric models are insufficient to represent lung nodule features.

Complex feature extraction is crucial for accurate lung nodule classification.

Abstract

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.