scholarly journals Predicting Surgical Phases using CNN-NARX Neural Network

2019 ◽  
Vol 5 (1) ◽  
pp. 405-407
Author(s):  
Nour Aldeen Jalal ◽  
Tamer Abdulbaki Alshirbaji ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Relevant Information ◽  
Surgical Tools ◽  
Use Of Data ◽  
Input Signals ◽  
Phase Prediction ◽  
Context Aware System ◽  
Laparoscopic Videos ◽  
Narx Neural Network

AbstractOnline recognition of surgical phases is essential to develop systems able to effectively conceive the workflow and provide relevant information to surgical staff during surgical procedures. These systems, known as context-aware system (CAS), are designed to assist surgeons, improve scheduling efficiency of operating rooms (ORs) and surgical team and promote a comprehensive perception and awareness of the OR. State-of-the-art studies for recognizing surgical phases have made use of data from different sources such as videos or binary usage signals from surgical tools. In this work, we propose a deep learning pipeline, namely a convolutional neural network (CNN) and a nonlinear autoregressive network with exogenous inputs (NARX), designed to predict surgical phases from laparoscopic videos. A convolutional neural network (CNN) is used to perform the tool classification task by automatically learning visual features from laparoscopic videos. The output of the CNN, which represents binary usage signals of surgical tools, is provided to a NARX neural network that performs a multistep-ahead predictions of surgical phases. Surgical phase prediction performance of the proposed pipeline was evaluated on a dataset of 80 cholecystectomy videos (Cholec80 dataset). Results show that the NARX model provides a good modelling of the temporal dependencies between surgical phases. However, more input signals are needed to improve the recognition accuracy.

scholarly journals Surgical Tool Classification in Laparoscopic Videos Using Convolutional Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 407-410 ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Training Data ◽  
Negative Effects ◽  
Surgical Tools ◽  
Surgical Tool ◽  
Context Aware System ◽  
Source Of Information ◽  
Laparoscopic Videos

AbstractLaparoscopic videos are a very important source of information which is inherently available in minimally invasive surgeries. Detecting surgical tools based on that videos have gained increasing interest due to its importance in developing a context-aware system. Such system can provide guidance assistance to the surgical team and optimise the processes inside the operating room. Convolutional neural network is a robust method to learn discriminative visual features and classify objects. As it expects a uniform distribution of data over classes, it fails to identify classes which are under-presented in the training data. In this work, loss-sensitive learning approach and resampling techniques were applied to counter the negative effects of imbalanced laparoscopic data on training the CNN model. The obtained results showed improvement in the classification performance especially for detecting surgical tools which are shortly used in the procedure.

scholarly journals Identification of Natural Selection in Genomic Data with Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background: With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to order to make inferences on demographic and adaptive processes using genomic data, Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results: The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Experiments performed on simulated data show that the proposed model can accurately predict neutral and selection processes on genomic data with more than 99% accuracy.

scholarly journals A convolutional neural network with a two-stage LSTM model for tool presence detection in laparoscopic videos

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Critical Role ◽  
Video Clip ◽  
Short Term ◽  
Two Stage ◽  
Surgical Tool ◽  
Long Short Term Memory ◽  
Laparoscopic Videos

AbstractSurgical tool presence detection in laparoscopic videos is a challenging problem that plays a critical role in developing context-aware systems in operating rooms (ORs). In this work, we propose a deep learning-based approach for detecting surgical tools in laparoscopic images using a convolutional neural network (CNN) in combination with two long short-term memory (LSTM) models. A pre-trained CNN model was trained to learn visual features from images. Then, LSTM was employed to include temporal information through a video clip of neighbour frames. Finally, the second LSTM was utilized to model temporal dependencies across the whole surgical video. Experimental evaluation has been conducted with the Cholec80 dataset to validate our approach. Results show that the most notable improvement is achieved after employing the two-stage LSTM model, and the proposed approach achieved better or similar performance compared with state-of-the-art methods.

scholarly journals Identification of natural selection in genomic data with deep convolutional neural network

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Training performed on simulated data show that the proposed model can accurately predict neutral and selection processes on portions of genomes taken from real populations with almost 90% accuracy.

Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression

2020 ◽  
Vol 19 (03) ◽  
pp. 2050024
Author(s):  
Abdoul-Dalibou Abdou ◽  
Ndeye Fatou Ngom ◽  
Oumar Niang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nonlinear Regression ◽  
Cardiac Arrhythmias ◽  
Heart Diseases ◽  
Heart Rhythm ◽  
Relevant Information ◽  
Biomedical Signal Processing ◽  
Ecg Signal ◽  
Extraction Step

In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.

scholarly journals Learning-based Orchestrator for Intelligent Software-defined Networking Controllers

2020 ◽  
Vol 11 (6) ◽  
pp. 17-30
Author(s):  
Imene Elloumi Zitouna
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Software Defined Networking ◽  
Relevant Information ◽  
Intelligent Software ◽  
Trade Offs ◽  
Networking Technology

This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we present this using Machine Learning in Software-Defined Networking technology. The first task consists of extracting relevant information from the Data flow generated from a SDN and using them to learn, to predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional Neural Network model to separate the classes that we’re working on. The result was very promising the model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to researchers looking for the trade-offs between SDN performance and IA customization.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
Sign in / Sign up

Export Citation Format

Share Document