ArticleEuropean radiology2020
Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- YOLOv11-MFF: A multi-scale frequency-adaptive fusion network for enhanced CXR anomaly detection.PloS one · 2025Article
- Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Supervised representation learning based on various levels of pediatric radiographic views for transfer learning.Scientific reports · 2024Article
- A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.Nature communications · 2024Article
- From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images.Cureus · 2023Review
- A systematic approach to deep learning-based nodule detection in chest radiographs.Scientific reports · 2023Article
- A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures.Frontiers in medicine · 2023Article
- AI-based radiodiagnosis using chest X-rays: A review.Frontiers in big data · 2023Review
- Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets.International journal of imaging systems and technology · 2021Article
- Reproducibility of abnormality detection on chest radiographs using convolutional neural network in paired radiographs obtained within a short-term interval.Scientific reports · 2020Article
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6 authors.
Funding
Abstract
objectivesTo investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs.
methodsWe 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.
resultsIn 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 < 0.05). In RetinaNet, matrix size 896 was significantly higher FOM (0.906) than others (0.329-0.832) (p < 0.05). For sensitivity of abnormalities, there was a tendency to increase sensitivity when lesion size increases. For small nodules (< 10 mm), the sensitivities were 0.418 and 0.409, whereas the sensitivities were 0.937 and 0.956 for masses. Matrix size 896 and 1344 in Mask R-CNN and matrix size 896 in RetinaNet showed significantly higher sensitivity than others (p < 0.05).
conclusionsMatrix size 896 had the highest performance for various sizes of abnormalities using different CNNs. The optimal matrix size of chest radiograph could improve CAD performance without additional training data. KEY POINTS: • Input matrix size significantly affected the performance of a deep learning-based CAD for detection of nodules or masses on chest radiographs. • The matrix size 896 showed the best performance in two different CNN detection models. • The optimal matrix size of chest radiographs could enhance CAD performance without additional training data.
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