scholarly journals Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5256
Author(s):  
Mohammed Isam Al-Hiyali ◽  
Norashikin Yahya ◽  
Ibrahima Faye ◽  
Ahmed Faeq Hussein
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Phase Synchronization ◽  
Binary Classification ◽  
Blood Oxygen Level Dependent ◽  
Autism Spectrum ◽  
Wavelet Coherence ◽  
Good Resolution ◽  
Classification Models ◽  
Multi Class Classification

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

scholarly journals Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5192
Author(s):  
Maira Moran ◽  
Marcelo Faria ◽  
Gilson Giraldi ◽  
Luciana Bastos ◽  
Larissa Oliveira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dental Caries ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Clinical Analysis ◽  
Diagnostic Process ◽  
Radiographic Evaluation ◽  
Classification Models ◽  
Approximal Caries ◽  
Lesion Severity

Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation—more specifically, bitewing images—are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.

scholarly journals Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5489
Author(s):  
Abdelrahman Ahmed ◽  
Sergio Toral ◽  
Khaled Shaalan ◽  
Yaser Hifny
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Call Center ◽  
Short Term Memory ◽  
Call Centers ◽  
Binary Classification ◽  
Speech Signal Processing ◽  
Baseline System ◽  
Long Short Term Memory ◽  
Current Systems

Measuring the productivity of an agent in a call center domain is a challenging task. Subjective measures are commonly used for evaluation in the current systems. In this paper, we propose an objective framework for modeling agent productivity for real estate call centers based on speech signal processing. The problem is formulated as a binary classification task using deep learning methods. We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. The result shows that the speech-based approach can lead to significant improvements (1.57% absolute improvements) over a robust text baseline system.

scholarly journals Implementation of Convolutional Neural Networks for Warp Detection in 3D Printed Components manufactured via Fused Filament Fabrication: A Bayesian-Based Automated Approach

2021 ◽  
Author(s):  
Aditya Saluja
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Closed Loop ◽  
Structural Integrity ◽  
Classification Models ◽  
Fused Filament Fabrication ◽  
3D Printed ◽  
Monitoring Architecture ◽  
Warp Deformation

Fused Filament Fabrication (FFF) is an additive manufacturing technique commonly used in industry to produce complicated structures sustainably. Although promising, the technology frequently suffers from defects, including warp deformation compromising the structural integrity of the component and, in extreme cases, the printer itself. To avoid the adverse effects of warp deformation, this thesis explores the implementation of deep neural networks to form a closed-loop in-process monitoring architecture using Convolutional Neural Networks (CNN) capable of pausing a printer once a warp is detected. Any neural network, including CNNs, depend on their hyperparameters. Hyperparameters can either be optimized using a manual or an automated approach. A manual approach, although easier to program, is often time-consuming, inaccurate and computationally inefficient, necessitating an automated approach. To evaluate this statement, classification models were optimized through both approaches and tested in a laboratory scaled manufacturing environment. The automated approach utilized a Bayesianbased optimizer yielding a mean accuracy of 100% significantly higher than 36% achieved by the other approach.

A CONVblock for Convolutional Neural Networks

2021 ◽  
pp. 100-113
Author(s):  
Hmidi Alaeddine ◽  
Malek Jihene
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Experimental Results ◽  
Classification Models ◽  
Deep Architecture ◽  
Improved Performance ◽  
Convolution Filters ◽  
Classification Database

The reduction in the size of convolution filters has been shown to be effective in image classification models. They make it possible to reduce the calculation and the number of parameters used in the operations of the convolution layer while increasing the efficiency of the representation. The authors present a deep architecture for classification with improved performance. The main objective of this architecture is to improve the main performances of the network thanks to a new design based on CONVblock. The proposal is evaluated on a classification database: CIFAR-10 and MNIST. The experimental results demonstrate the effectiveness of the proposed method. This architecture offers an error of 1.4% on CIFAR-10 and 0.055% on MNIST.

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

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Patrick Beyersdorffer ◽  
Wolfgang Kunert ◽  
Kai Jansen ◽  
Johanna Miller ◽  
Peter Wilhelm ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adverse Events ◽  
Convolutional Neural Networks ◽  
Surgical Training ◽  
Binary Classification ◽  
Image Data ◽  
Training Image ◽  
Training Environment ◽  
Data Set

Abstract Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

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