A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD
Joint Authors
Qadeer, Nouman
Zheng, Guangyuan
Han, Guanghui
Ma, Linjuan
Zhang, Fuquan
Zhao, Yanfeng
Zhao, Xinming
Zhou, Chunwu
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-15
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Purpose.
Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples.
To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD.
Methods.
From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples.
In addition, we randomly selected 2,000 non-lesion image blocks as negative samples.
We split the data 90% for training and 10% for testing.
We designed a DCGAN generative adversarial framework and trained it on the small sample set.
We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier.
Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached.
Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation.
Results.
Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples.
For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively.
The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%.
G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively.
Conclusion.
The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field.
Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.
American Psychological Association (APA)
Zheng, Guangyuan& Han, Guanghui& Qadeer, Nouman& Ma, Linjuan& Zhang, Fuquan& Zhao, Yanfeng…[et al.]. 2019. A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD. BioMed Research International،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1126768
Modern Language Association (MLA)
Zheng, Guangyuan…[et al.]. A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD. BioMed Research International No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1126768
American Medical Association (AMA)
Zheng, Guangyuan& Han, Guanghui& Qadeer, Nouman& Ma, Linjuan& Zhang, Fuquan& Zhao, Yanfeng…[et al.]. A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1126768
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126768