scholarly journals Real-Time Tracking of Guidewire Robot Tips Using Deep Convolutional Neural Networks on Successive Localized Frames

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 159743-159753 ◽  
Author(s):  
Ihsan Ullah ◽  
Philip Chikontwe ◽  
Sang Hyun Park
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Real Time Tracking

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

scholarly journals An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 153-162
Author(s):  
Gibson Kimutai ◽  
Alexander Ngenzi ◽  
Said Rutabayiro Ngoga ◽  
Rose C. Ramkat ◽  
Anna Förster
Keyword(s):  
Neural Networks ◽  
Internet Of Things ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Colour Change ◽  
Black Tea ◽  
Majority Voting ◽  
Raspberry Pi ◽  
Deep Convolutional Neural Networks ◽  
Precision And Accuracy

Abstract. Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.

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