scholarly journals Cardiac Abnormality Detection in 12-lead ECGs with Deep Convolutional Neural Networks Using Data Augmentation

2020 ◽  
Author(s):  
Lucas Weber ◽  
Maksym Gaiduk ◽  
Wilhelm Daniel Scherz ◽  
Ralf Seepold
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Cardiac Abnormality ◽  
Abnormality Detection ◽  
Deep Convolutional Neural Networks ◽  
Using Data

scholarly journals Understanding unconventional preprocessors in deep convolutional neural networks for face identification

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Chollette C. Olisah ◽  
Lyndon Smith
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Color Space ◽  
Back Propagation ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Deep Network

Abstract Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.

scholarly journals Real-time Ball Detection for Robocup Soccer Using Convolutional Neural Networks

2019 ◽  
Author(s):  
Lucas Ribeiro De Abreu ◽  
Reinaldo Augusto da Costa Bianchi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Average Precision ◽  
Critical System ◽  
Soccer Ball ◽  
Learning Techniques ◽  
Using Data ◽  
Vision Module ◽  
Ball Detection

The RoboCup Soccer is one of the largest competitions in the robotics field of research. It considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. In this paper, an approach using Convolutional Neural Networks for object detection is described. The soccer ball is the chosen object and three state-ofart convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an average precision of 0.972 with an intersection over union of 50% and 9.64 frames per second, running on CPU.

scholarly journals Genome annotation across species using deep convolutional neural networks

2020 ◽  
Vol 6 ◽  
pp. e278 ◽  
Author(s):  
Ghazaleh Khodabandelou ◽  
Etienne Routhier ◽  
Julien Mozziconacci
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Functional Role ◽  
Genomic Sequences ◽  
Sequence Motifs ◽  
Deep Convolutional Neural Networks ◽  
Genome Wide ◽  
Associated Proteins ◽  
Using Data ◽  
Genome Annotations

Application of deep neural network is a rapidly expanding field now reaching many disciplines including genomics. In particular, convolutional neural networks have been exploited for identifying the functional role of short genomic sequences. These approaches rely on gathering large sets of sequences with known functional role, extracting those sequences from whole-genome-annotations. These sets are then split into learning, test and validation sets in order to train the networks. While the obtained networks perform well on validation sets, they often perform poorly when applied on whole genomes in which the ratio of positive over negative examples can be very different than in the training set. We here address this issue by assessing the genome-wide performance of networks trained with sets exhibiting different ratios of positive to negative examples. As a case study, we use sequences encompassing gene starts from the RefGene database as positive examples and random genomic sequences as negative examples. We then demonstrate that models trained using data from one organism can be used to predict gene-start sites in a related species, when using training sets providing good genome-wide performance. This cross-species application of convolutional neural networks provides a new way to annotate any genome from existing high-quality annotations in a related reference species. It also provides a way to determine whether the sequence motifs recognised by chromatin-associated proteins in different species are conserved or not.

Pedestrian-Motorcycle Binary Classification Using Data Augmentation and Convolutional Neural Networks

2019 ◽  
pp. 725-737
Author(s):  
Robert Kerwin C. Billones ◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim ◽  
Edwin Sybingco ◽  
Alexis M. Fillone ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Binary Classification ◽  
Using Data

scholarly journals Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation

2019 ◽  
Vol 118 ◽  
pp. 315-328 ◽  
Author(s):  
Anabel Gómez-Ríos ◽  
Siham Tabik ◽  
Julián Luengo ◽  
ASM Shihavuddin ◽  
Bartosz Krawczyk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Deep Convolutional Neural Networks
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