Image Resolution Enhancement Technology Based on Deep Neural Network

2020 ◽  
pp. 687-693
Author(s):  
Ping Yu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Resolution Enhancement ◽  
Image Resolution ◽  
Enhancement Technology

scholarly journals Adaptive Cycle Spinning Cellular Neural Network for Image Resolution Enhancement

2014 ◽  
Vol 18 (4) ◽  
pp. 173-176
Author(s):  
Kei Chiba ◽  
Tsuyoshi Otake ◽  
Hisashi Aomori ◽  
Nobuaki Takahashi ◽  
Mamoru Tanaka
Keyword(s):  
Neural Network ◽  
Resolution Enhancement ◽  
Cellular Neural Network ◽  
Image Resolution ◽  
Adaptive Cycle ◽  
Cycle Spinning

scholarly journals Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3300
Author(s):  
Jing Gong ◽  
Jiyu Liu ◽  
Haiming Li ◽  
Hui Zhu ◽  
Tingting Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Stratification ◽  
Lung Adenocarcinoma ◽  
Deep Neural Network ◽  
Characteristic Curve ◽  
Ct Images ◽  
Image Resolution ◽  
Positive Trend ◽  
Slice Thickness ◽  
Two Stage

This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens. A two-stage deep neural network was developed based on the 3D residual network and atrous convolution module to diagnose benign and malignant GGNs (Task1) and classify between invasive adenocarcinoma (IA) and non-IA for these malignant GGNs (Task2). A multi-reader multi-case observer study with six board-certified radiologists’ (average experience 11 years, range 2–28 years) participation was conducted to evaluate the model capability. DNN yielded area under the receiver operating characteristic curve (AUC) values of 0.76 ± 0.03 (95% confidence interval (CI): (0.69, 0.82)) and 0.96 ± 0.02 (95% CI: (0.92, 0.98)) for Task1 and Task2, which were equivalent to or higher than radiologists in the senior group with average AUC values of 0.76 and 0.95, respectively (p > 0.05). With the CT image slice thickness increasing from 1.15 mm ± 0.36 to 1.73 mm ± 0.64, DNN performance decreased 0.08 and 0.22 for the two tasks. The results demonstrated (1) a positive trend between the diagnostic performance and radiologist’s experience, (2) the DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution decreased model performance in predicting the risks of GGNs. Once tested prospectively in clinical practice, the DNN could have the potential to assist doctors in precision diagnosis and treatment of early lung adenocarcinoma.

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