scholarly journals A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss

2020 ◽  
Vol 6 ◽  
pp. e324
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Mei Yang ◽  
Feng Hong ◽  
Chun Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Method ◽  
Deep Neural Network ◽  
Morphological Features ◽  
Multilayer Perceptrons ◽  
Medical Monitoring ◽  
Rr Intervals ◽  
Heartbeat Classification

Background Heart arrhythmia, as one of the most important cardiovascular diseases (CVDs), has gained wide attention in the past two decades. The article proposes a hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. Methods In the method, a convolution neural network is used to extract the morphological features. The reason behind this is that the morphological characteristics of patients have inter-patient variations, which makes it difficult to accurately describe using traditional hand-craft ways. Then the extracted morphological features are combined with the RR intervals features and input into the multilayer perceptron for heartbeat classification. The RR intervals features contain the dynamic information of the heartbeat. Furthermore, considering that the heartbeat classes are imbalanced and would lead to the poor performance of minority classes, a focal loss is introduced to resolve the problem in the article. Results Tested using the MIT-BIH arrhythmia database, our method achieves an overall positive predictive value of 64.68%, sensitivity of 68.55%, f1-score of 66.09%, and accuracy of 96.27%. Compared with existing works, our method significantly improves the performance of heartbeat classification. Conclusions Our method is simple yet effective, which is potentially used for personal automatic heartbeat classification in remote medical monitoring. The source code is provided on https://github.com/JackAndCole/Deep-Neural-Network-For-Heartbeat-Classification.

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

Deep distributed convolutional neural networks: Universality

2018 ◽  
Vol 16 (06) ◽  
pp. 895-919 ◽  
Author(s):  
Ding-Xuan Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Mathematical Foundation ◽  
Deep Convolutional Neural Networks ◽  
The Family ◽  
Fully Connected

Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory.

scholarly journals Hybrid method of conventional neural network training

2021 ◽  
Vol 20 (2) ◽  
pp. 463-490
Author(s):  
Andrey Golubinskiy ◽  
Andrey Tolstykh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Hybrid Method ◽  
Neural Network Training ◽  
First Order ◽  
Network Training ◽  
First Order Methods

The paper proposes a hybrid method for training convolutional neural networks. The method consists of combining second and first-order methods for different elements of the architecture of a convolutional neural network. The hybrid convolution neural network training method allows to achieve significantly better convergence compared to Adam; however, it requires fewer computational operations to implement. Using the proposed method, it is possible to train networks on which learning paralysis occurs when using first-order methods. Moreover, the proposed method could adjust its computational complexity to the hardware on which the computation is performed; at the same time, the hybrid method allows using the mini-packet learning approach. The analysis of the ratio of computations between convolutional neural networks and fully connected artificial neural networks is presented. The mathematical apparatus of error optimization of artificial neural networks is considered, including the method of backpropagation of the error, the Levenberg-Marquardt algorithm. The main limitations of these methods that arise when training a convolutional neural network are analyzed. The analysis of the stability of the proposed method when the initialization parameters are changed. The results of the applicability of the method in various problems are presented.

scholarly journals Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

2019 ◽  
Author(s):  
Marek A. Pedziwiatr ◽  
Matthias Kümmerer ◽  
Thomas S.A. Wallis ◽  
Matthias Bethge ◽  
Christoph Teufel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Eye Movements ◽  
Convolutional Neural Networks ◽  
Deep Neural Network ◽  
Image Features ◽  
Human Vision ◽  
Deep Convolutional Neural Networks ◽  
High Level

AbstractEye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic importance across an image, have recently been proposed to support the hypothesis that meaning rather than image features guide human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II – a deep neural network trained to predict fixations based on high-level features rather than meaning – outperforms MMs. Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images.

scholarly journals Face recognition system Using Deep Neural Network with Convolutional Neural Networks

2019 ◽  
Vol 1235 ◽  
pp. 012004
Author(s):  
Erick Fernando ◽  
Denny Andwiyan ◽  
Dina Fitria Murad ◽  
Derist Touriano ◽  
Muhamad Irsan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Deep Neural Network ◽  
Recognition System ◽  
Face Recognition System
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