Affiliations: [a] Department of Computer Science, City University of New York, the Graduate Center, NY, USA
| [b] Department of Computer Science, City University of New York at CSI, NY, USA
| [c] Department of Radiology, State University of New York at Stony Brook, NY, USA
| [d] Department of Engineering Science and Physics, City University of New York at CSI, NY, USA
Correspondence:
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Corresponding author: Zhengrong Liang, Department of Radiology, Electrical and Computer Engineering, and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. E-mail: [email protected].
Abstract: BACKGROUND:Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets. On the other hand, the engineered features have been widely studied. OBJECTIVE:We proposed a novel nodule CADe which aims to relieve the challenge by the use of available engineered features to prevent convolution neural networks (CNN) from overfitting under dataset limitation and reduce the running-time complexity of self-learning. METHODS:The CADe methodology infuses adequately the engineered features, particularly texture features, into the deep learning process. RESULTS:The methodology was validated on 208 patients with at least one juxta-pleural nodule from the public LIDC-IDRI database. Results demonstrated that the methodology achieves a sensitivity of 88% with 1.9 false positives per scan and a sensitivity of 94.01% with 4.01 false positives per scan. CONCLUSIONS:The methodology shows high performance compared with the state-of-the-art results, in terms of accuracy and efficiency, from both existing CNN-based approaches and engineered feature-based classifications.
Keywords: Computer aided detection (CADe), computed tomography (CT) imaging, pulmonary nodules, deep learning, image features analysis
