Lung Nodule Classification Using Deep Features in CT Images
Abstract
Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of the total cancer related deaths. A large number of cases are encountered by radiologists on a daily basis for initial diagnosis. Computer-aided diagnosis (CAD) systems can assist radiologists by offering a "second opinion" and making the whole process faster. We propose a CAD system which uses deep features extracted from an auto encoder to classify lung nodules as either malignant or benign. We use 4303 instances containing 4323 nodules from the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset to obtain an overall accuracy of 75.01% with a sensitivity of 83.35% and false positive of 0.39/patient over a 10 fold cross validation.
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- Authors
- Kumar, D., Wong, A., & Clausi, D. A.
- Affiliations
- University of Waterloo (CA)
- Venue
- 2015 12th Conference on Computer and Robot Vision
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 133-138
- OpenAlex
- W1898227994
- Cited by
- 398
- References
- 22
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 28 · 2023: 47 · 2022: 46 · 2021: 45 · 2020: 48 · 2019: 55 · 2018: 45 · 2017: 32 · 2016: 17 · 2015: 1
- FWCI
- 17.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Canadian Conference on Computer and Robot Vision (publishedVersion)
- Funders
- Foundation for the National Institutes of Health
- Canada Research Chairs
- Natural Sciences and Engineering Research Council of Canada
- Ontario Ministry of Research and Innovation
- National Cancer Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- lung nodule classification, computer-aided diagnosis, autoencoder features, deep features, early detection of lung cancer, LIDC dataset, CT images
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