Real-Time Image Recognition System Based on an Embedded Heterogeneous Computer and Deep Convolutional Neural Networks for Deployment in Constrained Environments

2019 ◽  
Author(s):  
Maycon Douglas da Silva Carvalho ◽  
Fabian Koark ◽  
Carl Rheinländer ◽  
Norbert Wehn
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Recognition System ◽  
Deep Convolutional Neural Networks ◽  
Real Time Image ◽  
Constrained Environments ◽  
Time Image

Real-Time Image Deblurring and Super Resolution Using Convolutional Neural Networks

2021 ◽  
pp. 381-394
Author(s):  
Nidhi Galgali ◽  
Melita Maria Pereira ◽  
N. K. Likitha ◽  
B. R. Madhushri ◽  
E. S. Vani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Deblurring ◽  
Real Time Image ◽  
Time Image

A Real-Time Image Recognition System for Tiny Autonomous Mobile Robots

2005 ◽  
Vol 29 (2-3) ◽  
pp. 247-261 ◽  
Author(s):  
Stefan Mahlknecht ◽  
Roland Oberhammer ◽  
Gregor Novak
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Image Recognition ◽  
Recognition System ◽  
Autonomous Mobile Robots ◽  
Real Time Image ◽  
Time Image

scholarly journals Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks

2021 ◽  
Author(s):  
Biluo Shen ◽  
Zhe Zhang ◽  
Xiaojing Shi ◽  
Caiguang Cao ◽  
Zeyu Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Fluorescence Imaging ◽  
Convolutional Neural Networks ◽  
Frozen Section ◽  
Fluorescent Imaging ◽  
Pathological Diagnosis ◽  
Pathological Examination ◽  
Deep Convolutional Neural Networks ◽  
Fluorescence Images

Abstract Purpose Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon’s visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery. Methods Glioma patients (N = 23) were enrolled and injected with indocyanine green for fluorescence image–guided surgery. Tissue samples (N = 1874) were harvested from surgery of these patients, and the second near-infrared window (NIR-II, 1000–1700 nm) fluorescence images were obtained. Deep convolutional neural networks (CNNs) combined with NIR-II fluorescence imaging (named as FL-CNN) were explored to automatically provide pathological diagnosis of glioma in situ in real-time during patient surgery. The pathological examination results were used as the gold standard. Results The developed FL-CNN achieved the area under the curve (AUC) of 0.945. Comparing to neurosurgeons’ judgment, with the same level of specificity >80%, FL-CNN achieved a much higher sensitivity (93.8% versus 82.0%, P < 0.001) with zero time overhead. Further experiments demonstrated that FL-CNN corrected >70% of the errors made by neurosurgeons. FL-CNN was also able to rapidly predict grade and Ki-67 level (AUC 0.810 and 0.625) of tumor specimens intraoperatively. Conclusion Our study demonstrates that deep CNNs are better at capturing important information from fluorescence images than surgeons’ evaluation during patient surgery. FL-CNN is highly promising to provide pathological diagnosis intraoperatively and assist neurosurgeons to obtain maximum resection safely. Trial registration ChiCTR ChiCTR2000029402. Registered 29 January 2020, retrospectively registered

Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy

2016 ◽  
Vol 83 (3) ◽  
pp. 643-649 ◽  
Author(s):  
Yoko Kominami ◽  
Shigeto Yoshida ◽  
Shinji Tanaka ◽  
Yoji Sanomura ◽  
Tsubasa Hirakawa ◽  
...  
Keyword(s):  
Image Recognition ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Recognition System ◽  
Colorectal Polyp ◽  
Magnifying Colonoscopy ◽  
Computer Aided ◽  
Real Time Image ◽  
Time Image ◽  
Aided Diagnosis
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