Work

Deep Learning for the Classification of Lung Nodules

Year: 2016

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1611.06651

Authors: Yang, He, Yu, Hengyong, Wang, Ge

Institutions: The Ohio State University, University of Massachusetts Lowell, Rensselaer Polytechnic Institute

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering