scholarly journals Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data (Preprint)

2019 ◽  
Author(s):  
Yang Cao ◽  
Scott Montgomery ◽  
Johan Ottosson ◽  
Erik Näslund ◽  
Erik Stenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bariatric Surgery ◽  
Deep Learning ◽  
Postoperative Complications ◽  
Obesity Surgery ◽  
Test Data ◽  
Registry Data ◽  
Training Data ◽  
Complications After Bariatric Surgery

BACKGROUND Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. OBJECTIVE This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. METHODS Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. RESULTS In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. CONCLUSIONS MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information. CLINICALTRIAL

scholarly journals Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data

10.2196/15992 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15992 ◽  
Author(s):  
Yang Cao ◽  
Scott Montgomery ◽  
Johan Ottosson ◽  
Erik Näslund ◽  
Erik Stenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bariatric Surgery ◽  
Deep Learning ◽  
Postoperative Complications ◽  
Obesity Surgery ◽  
Test Data ◽  
Registry Data ◽  
Training Data ◽  
Complications After Bariatric Surgery

Background Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. Objective This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. Methods Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. Results In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. Conclusions MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Neural network activation similarity: a new measure to assist decision making in chemical toxicology

2020 ◽  
Vol 11 (28) ◽  
pp. 7335-7348 ◽  
Author(s):  
Timothy E. H. Allen ◽  
Andrew J. Wedlake ◽  
Elena Gelžinytė ◽  
Charles Gong ◽  
Jonathan M. Goodman ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Test Data ◽  
High Performance ◽  
Chemical Binding ◽  
Biological Targets ◽  
Network Activation ◽  
Chemical Toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).

Elimination of the Collision States of the Effectors of Industrial Robots by Application of Neural Networks

2015 ◽  
Vol 798 ◽  
pp. 276-281
Author(s):  
Erik Prada ◽  
Lenka Baločková ◽  
Michael Valášek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Test Data ◽  
Learning Algorithms ◽  
Robotic System ◽  
Industrial Robots ◽  
Training Data ◽  
Methods Of Learning ◽  
In The Beginning

The article deals with the usage of methods of learning algorithms of neural networks for solving of collision states problem within multi-robotic cooperation. Nowadays, multi-robotic cooperation is a highly used method of work of two or more industrial robots. The requirements for elimination of collision states are getting more difficult when the multi-robotic system is more complicated. Methods of neural networks provide suitable tools for solving of complex cooperating problems. In the beginning of the article, we discuss the term “collision state” and the possibilities of its solving. In the following chapter, we discuss the theory of neural networks and learning algorithms, which we applied in solving of the collision states. In the final chapter, we implemented the practical verification of the model neural network in JSNN programme. It consisted of creating and learning of the training data and subsequent verification of the test data.

Failure of the Obesity Surgery Mortality Risk Score (OS-MRS) to Predict Postoperative Complications After Bariatric Surgery. A Single-Center Series and Systematic Review

2016 ◽  
Vol 27 (6) ◽  
pp. 1423-1429 ◽  
Author(s):  
Maria Luisa García-García ◽  
Juan Gervasio Martín-Lorenzo ◽  
Ramón Lirón-Ruiz ◽  
José Antonio Torralba-Martínez ◽  
José Antonio García-López ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bariatric Surgery ◽  
Postoperative Complications ◽  
Obesity Surgery ◽  
Risk Score ◽  
Mortality Risk ◽  
Single Center ◽  
Complications After Bariatric Surgery ◽  
Surgery Mortality

scholarly journals Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification

Mekatronika ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 80-86
Author(s):  
Ooi Peng Toon ◽  
Muhammad Aizzat Zakaria ◽  
Ahmad Fakhri Ab. Nasir ◽  
Anwar P.P. Abdul Majeed ◽  
Chung Young Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
South America ◽  
Solanum Lycopersicum ◽  
Training Data ◽  
New Classification ◽  
The Past ◽  
Testing Data ◽  
Harvesting Robot

Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.

scholarly journals Plastic Gasket Defect Detection Based on Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xieyi Chen ◽  
Dongyun Wang ◽  
Jinjun Shao ◽  
Jun Fan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Defect Detection ◽  
Data Augmentation ◽  
Surface Defects ◽  
Visual Detection ◽  
Training Data

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

scholarly journals Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Qiang Fang ◽  
Clemente Ibarra-Castanedo ◽  
Xavier Maldague
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Deep Learning ◽  
Infrared Thermography ◽  
Learning Algorithm ◽  
Synthetic Data ◽  
Training Data ◽  
Experimental Database ◽  
The Neural Network

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

scholarly journals Image classification of different clove (Syzygium aromaticum) quality using deep learning method with convolutional neural network algorithm

2021 ◽  
Vol 905 (1) ◽  
pp. 012018
Author(s):  
I Y Prayogi ◽  
Sandra ◽  
Y Hendrawan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Training Data ◽  
Syzygium Aromaticum ◽  
Learning Method ◽  
Validation Data ◽  
Reading Accuracy

Abstract The objective of this study is to classify the quality of dried clove flowers using deep learning method with Convolutional Neural Network (CNN) algorithm, and also to perform the sensitivity analysis of CNN hyperparameters to obtain best model for clove quality classification process. The quality of clove as raw material in this study was determined according to SNI 3392-1994 by PT. Perkebunan Nusantara XII Pancusari Plantation, Malang, East Java, Indonesia. In total 1,600 images of dried clove flower were divided into 4 qualities. Each clove quality has 225 training data, 75 validation data, and 100 test data. The first step of this study is to build CNN model architecture as first model. The result of that model gives 65.25% reading accuracy. The second step is to analyze CNN sensitivity or CNN hyperparameter on the first model. The best value of CNN hyperparameter in each step then to be used in the next stage. Finally, after CNN hyperparameter carried out the reading accuracy of the test data is improved to 87.75%.

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks

2020 ◽  
Vol 42 (4-5) ◽  
pp. 213-220 ◽  
Author(s):  
Tomoyuki Fujioka ◽  
Leona Katsuta ◽  
Kazunori Kubota ◽  
Mio Mori ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Shear Wave ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Diagnostic Performance ◽  
Shear Wave Elastography ◽  
Training Data ◽  
Breast Masses ◽  
Malignant Breast

We aimed to use deep learning with convolutional neural networks (CNNs) to discriminate images of benign and malignant breast masses on ultrasound shear wave elastography (SWE). We retrospectively gathered 158 images of benign masses and 146 images of malignant masses as training data for SWE. A deep learning model was constructed using several CNN architectures (Xception, InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and NASNetMobile) with 50, 100, and 200 epochs. We analyzed SWE images of 38 benign masses and 35 malignant masses as test data. Two radiologists interpreted these test data through a consensus reading using a 5-point visual color assessment (SWEc) and the mean elasticity value (in kPa) (SWEe). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. The best CNN model (which was DenseNet169 with 100 epochs), SWEc, and SWEe had a sensitivity of 0.857, 0.829, and 0.914 and a specificity of 0.789, 0.737, and 0.763 respectively. The CNNs exhibited a mean AUC of 0.870 (range, 0.844–0.898), and SWEc and SWEe had an AUC of 0.821 and 0.855. The CNNs had an equal or better diagnostic performance compared with radiologist readings. DenseNet169 with 100 epochs, Xception with 50 epochs, and Xception with 100 epochs had a better diagnostic performance compared with SWEc ( P = 0.018–0.037). Deep learning with CNNs exhibited equal or higher AUC compared with radiologists when discriminating benign from malignant breast masses on ultrasound SWE.

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