CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images

Biomedical Engineering Online
Patrice MonkamWei Qian

Abstract

Early and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for detecting micro-nodules (diameter < 3 mm), which increases the radiologists' workload and causes unnecessary anxiety for the patients. To decrease the false positive rate, we propose to use CNN models to discriminate between pulmonary micro-nodules and non-nodules from CT image patches. A total of 13,179 micro-nodules and 21,315 non-nodules marked by radiologists are extracted with three different patch sizes (16 × 16, 32 × 32 and 64 × 64) from LIDC/IDRI database and used in the experiments. Three CNN models with different depths (1, 2 or 4 convolutional layers) are designed; their performances are evaluated by the fivefold cross-validation in term of the accuracy, area under the curve (AUC), F-score and sensitivity. The network parameters are also optimized. It is found that the performance of the CNN models is greatly dependent on the patches size and the number of convolutional layers. The CNN model with two convolutional layers presented the best performance in case of 32 × 32 patches size, a...Continue Reading

References

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Citations

Apr 12, 2020·Veterinary Research·Abigail R TrachtmanGiuseppe Marruchella
Jun 23, 2019·Journal of X-ray Science and Technology·Xinzhuo ZhaoJianjun Sun
Apr 23, 2020·Computational Intelligence and Neuroscience·Panpan WuBjörn Schuller
Nov 4, 2020·International Journal of Computer Assisted Radiology and Surgery·Rekka MastouriSaoussen Hantous-Zannad
Jun 11, 2020·Cancer Metastasis Reviews·Shailesh Kumar ThakurJaytrilok Choudhary

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Matlab
GoogLeNet
AlexNet
Windows
VGG
Net

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