Comparison of Convolutional Neural Networks in Real-Time Monitoring of Aquaculture Water State

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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FT-RCNN: Real-time Visual Face Tracking with Region-based Convolutional Neural Networks

2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) ◽

10.1109/fg47880.2020.00040 ◽

2020 ◽

Author(s):

Yiming Lin ◽

Jie Shen ◽

Shiyang Cheng ◽

Maja Pantic

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Face Tracking

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Residual Convolutional Neural Networks Model For Image Denoising On Real Time

2020 International Conference on Control, Automation and Diagnosis (ICCAD) ◽

10.1109/iccad49821.2020.9260531 ◽

2020 ◽

Author(s):

Rania KALLEL ◽

Aicha BEN SALEM ◽

Henda Ben Ghezala

Keyword(s):

Neural Networks ◽

Real Time ◽

Image Denoising ◽

Convolutional Neural Networks

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A Real-Time IoT Based Arrhythmia Classifier Using Convolutional Neural Networks

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) ◽

10.1109/discover50404.2020.9278059 ◽

2020 ◽

Author(s):

Trivikrama Bhat ◽

Akanksha ◽

Shrikara ◽

Shreya Bhat ◽

Manoj T

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks

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

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05326-y ◽

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

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