Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach

2019 ◽  
Vol 34 (11) ◽  
pp. 4924-4931 ◽  
Author(s):  
Daichi Kitaguchi ◽  
Nobuyoshi Takeshita ◽  
Hiroki Matsuzaki ◽  
Hiroaki Takano ◽  
Yohei Owada ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Learning Approach ◽  
Laparoscopic Sigmoidectomy ◽  
Phase Recognition

scholarly journals Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach

2018 ◽  
Vol 132 ◽  
pp. 679-688 ◽  
Author(s):  
Sakshi Indolia ◽  
Anil Kumar Goswami ◽  
S.P. Mishra ◽  
Pooja Asopa
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Conceptual Understanding ◽  
Learning Approach

scholarly journals Investigation of optimal configurations of a convolutional neural network for the identification of objects in real-time

2019 ◽  
pp. 417-423
Author(s):  
M A Isayev ◽  
D A Savelyev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
State Of The Art ◽  
Average Precision ◽  
The Core ◽  
Particular Solution ◽  
Optimal Configurations

The comparison of different convolutional neural networks which are the core of the most actual solutions in the computer vision area is considers in hhe paper. The study includes benchmarks of this state-of-the-art solutions by some criteria, such as mAP (mean average precision), FPS (frames per seconds), for the possibility of real-time usability. It is concluded on the best convolutional neural network model and deep learning methods that were used at particular solution.

scholarly journals Large-Scale Text Classification Using Scope-Based Convolutional Neural Network: A Deep Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171548-171558 ◽  
Author(s):  
Jiaying Wang ◽  
Yaxin Li ◽  
Jing Shan ◽  
Jinling Bao ◽  
Chuanyu Zong ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Large Scale ◽  
Learning Approach

scholarly journals Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

2020 ◽  
Vol 10 (21) ◽  
pp. 7448
Author(s):  
Jorge Felipe Gaviria ◽  
Alejandra Escalante-Perez ◽  
Juan Camilo Castiblanco ◽  
Nicolas Vergara ◽  
Valentina Parra-Garces ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

scholarly journals XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3137
Author(s):  
Kevin Fauvel ◽  
Tao Lin ◽  
Véronique Masson ◽  
Élisa Fromont ◽  
Alexandre Termier
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Input Data ◽  
State Of The Art ◽  
Multivariate Time Series ◽  
Learning Approach ◽  
Multiple Domains ◽  
Post Hoc

Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.

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