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European Radiology

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01-09-2020 | Chest

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

Authors: Young-Gon Kim, Sang Min Lee, Kyung Hee Lee, Ryoungwoo Jang, Joon Beom Seo, Namkug Kim

Published in: European Radiology | Issue 9/2020

Abstract

Objectives

To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs.

Methods

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.

Results

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 < 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).

Conclusions

Matrix 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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Title: Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning
Authors: Young-Gon Kim
Sang Min Lee
Kyung Hee Lee
Ryoungwoo Jang
Joon Beom Seo
Namkug Kim
Publication date: 01-09-2020
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 9/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06892-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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