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<p>Purpose: To develop a novel computer-aided diagnosis (CAD) pulmonary nodule detection
system that can not only perform real-time detection but also characterize quantitative
nodule information based on deep learning methods.</p><p>Method: We constructed a
convolutional neural network (CNN) for automated pulmonary nodule detection and characterization.
Nodule detection was accomplished by customizing a detection algorithm (YOLO v3),
which comprised of a feature extractor and a bounding box generator. The feature extractor
had 19 convolutional layers with 7 residual shortcut connections to extract features
on input images at three different down-sampling scales (i.e. 4, 8, and 16). The bounding
box generator had 7 convolutional layers to determine the location and size of each
detected nodule. A python-based characterization system was then developed to characterize
size, diameter, and central coordinates of each detected nodule within the generated
bounding box. This characterization system applied a non-maximum suppression algorithm
to exclude nodules below true positive probability threshold. The system was trained
and validated using ten-fold cross-validation with 300 CT scans from XCAT simulation
and 888 patient CT scans from LIDC–IDRI public dataset, separately. System performance
was evaluated using Free-Response Receiver Operating Characteristic (FROC) analysis,
competition performance metric (CPM) score, as well as precision analysis of central
coordinates and diameters. </p><p>Result: The developed CAD system achieved CPM scores
of 0.99 in the simulation image study and 0.873 in the public database study. The
average performance time per image was less than 0.1 second. Compared with ground
truth data, the detection precision in diameter were 0.26 mm using simulated images
and 1.05 mm using public database, while the precision in central coordinate were
0.76 mm and 1.44 mm, respectively. </p><p>Conclusion: Preliminary evaluation showed
that our proposed CAD system using deep learning methods was robust and achieved real-time
nodule detection with high accuracy and characterization with high precision.</p>
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