Abstract
To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules. The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation. Results for candidate generation on the test data indicated a detection rate of 94.77% with 30.39 false positives per scan, while the test results for false positive reduction exhibited a sensitivity of 94.21% with 1.789 false positives per scan. The overall system detection rate on the test data was 89.29% with 1.789 false positives per scan. An extensive and rigorous validation was conducted to assess the performance of the proposed system. The system demonstrated a novel combination of 3D deep neural network architectures and demonstrates the use of deep learning for both candidate generation and false positive reduction to be evaluated with a substantial test dataset. The results strongly support the ability of deep learning pulmonary nodule detection systems to generalize to unseen data. The source code and trained model weights have been made available. A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance.
Highlights
Lung cancer is the leading cause of cancer mortality throughout the world.[1]
The Lung Image Database Consortium (LIDC)-Image Database Resource Initiative (IDRI) repository is comprised of 1018 openly available computed tomography (CT) scans collected from 5 participating institutions with each of the scans including annotations by 4 radiologists
The LUNA16 dataset[6] was created in part to address this issue. It is a collection of 888 thin-slice CT scans of consistent slice spacing from the LIDC-IDRI dataset.[54]
Summary
Lung cancer is the leading cause of cancer mortality throughout the world.[1]. Regular screening of high-risk individuals using low-dose computed tomography (CT) has been shown to reduce mortality in lung cancer patients.[2]. Computer-aided detection (CAD) systems have the potential to aid radiologists in lung cancer screening by reducing reading times or acting as a second reader
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More From: Journal of the American Medical Informatics Association
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