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Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning
- Source :
- European Radiology. 30:4943-4951
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. We retrospectively collected 2088 abnormal (nodule/mass) and 352 normal chest radiographs from two institutions. Three thoracic radiologists drew 2758 abnormalities regions. A total of 1736 abnormal chest radiographs were used for training and tuning convolutional neural networks (CNNs). The remaining 352 abnormal and 352 normal chest radiographs were used as a test set. Two CNNs (Mask R-CNN and RetinaNet) were selected to validate the effects of the squared different matrix size of chest radiograph (256, 448, 896, 1344, and 1792). For comparison, figure of merit (FOM) of jackknife free-response receiver operating curve and sensitivity were obtained. In Mask R-CNN, matrix size 896 and 1344 achieved significantly higher FOM (0.869 and 0.856, respectively) for detecting abnormalities than 256, 448, and 1792 (0.667–0.820) (p
- Subjects :
- Male
medicine.medical_specialty
Lung Neoplasms
Radiography
030218 nuclear medicine & medical imaging
03 medical and health sciences
Deep Learning
0302 clinical medicine
Humans
Medicine
Radiology, Nuclear Medicine and imaging
Diagnosis, Computer-Assisted
Lung
Aged
Retrospective Studies
Neuroradiology
Receiver operating characteristic
medicine.diagnostic_test
business.industry
Deep learning
Ultrasound
Solitary Pulmonary Nodule
Nodule (medicine)
General Medicine
Middle Aged
medicine.anatomical_structure
030220 oncology & carcinogenesis
Radiographic Image Interpretation, Computer-Assisted
Female
Radiography, Thoracic
Neural Networks, Computer
Radiology
Artificial intelligence
medicine.symptom
business
Chest radiograph
Precancerous Conditions
Subjects
Details
- ISSN :
- 14321084 and 09387994
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- European Radiology
- Accession number :
- edsair.doi.dedup.....72f0883cb2400d127f82b79c70cbd19f