Loading [MathJax]/extensions/TeX/ietmacros.js
Lung nodule detection in CT images using deep convolutional neural networks | IEEE Conference Publication | IEEE Xplore

Lung nodule detection in CT images using deep convolutional neural networks


Abstract:

Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improv...Show More

Abstract:

Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.
Date of Conference: 24-29 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2161-4407
Conference Location: Vancouver, BC, Canada

References

References is not available for this document.