scholarly journals Avaliação de dor em expressão facial neonatal por meio de redes neurais profundas

2020 ◽  
Author(s):  
◽  
L. F. Buzuti
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pain Assessment ◽  
Neural Models ◽  
External Variable ◽  
Neonatal Pain ◽  
Face Images ◽  
Average Accuracy ◽  
Late Intervention ◽  
Quantitative Results

Neonatal pain assessment might suffer variation among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to develop computational tools of pain assessment, less subjective and susceptible to external variable influences. Deep learning models, especially Convolutional Neural Networks, have gained ground in the last decade, due to many successful applications in image analysis, object recognitions and human emotion recognitions. In this context, the general aim this dissertation was analyse quantitatively and qualitatively models of Convolutional Neural Networks in the task neonatal pain classification through a computacional framework based in face images of two distinct databases (an international, named COPE, and other national, named UNIFESP). How specific aims were implemented, evaluated and compared the performance of three existent models used in literature: Neonatal Convolutional Neural Network (N-CNN) and two type of ResNet50 models. The quantitative results showed the excellence of N-CNN to neonatal pain assessment automatic, with average accuracy of 87.2% and 78.7% for the databases COPE and UNIFESP, respectively. However, the quantitative analysis showed that all neural models evaluated, including N-CNN models, can learn artifacts from the imagens and not variation discriminating in faces, thus showed the necessity more studies to apply this models in clinical practice

scholarly journals Neonatal Pain Assessment From Facial Expression Using Deep Neural Networks

2020 ◽  
Author(s):  
Lucas Buzuti ◽  
Tatiany Heideirich ◽  
Marina Barros ◽  
Ruth Guinsburg ◽  
Carlos Thomaz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pain Assessment ◽  
Health Professionals ◽  
Deep Neural Networks ◽  
Assessment Tools ◽  
Current Pattern ◽  
External Variable ◽  
Neonatal Pain ◽  
Late Intervention

Currently, neonatal pain assessment varies among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to understand the deficiencies of the current pattern of pain assessment tools in order to develop new ones, less subjective and susceptible to external variable influences. The aim of this paper is to investigate neonatal pain assessment using two models of Deep Learning: Neonatal Convolutional Neural Network trained end-to-end and ResNet trained using Transfer Learning. We used for training two distinct databases (COPE and Unifesp) and our results showed that the use of multi-racial databases might improve the evaluation of automatic models of neonatal pain assessment.

Convolutional Neural Networks for Neonatal Pain Assessment

2019 ◽  
Vol 1 (3) ◽  
pp. 192-200 ◽  
Author(s):  
Ghada Zamzmi ◽  
Rahul Paul ◽  
Md. Sirajus Salekin ◽  
Dmitry Goldgof ◽  
Rangachar Kasturi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pain Assessment ◽  
Neonatal Pain

scholarly journals Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

2019 ◽  
Vol 11 (12) ◽  
pp. 1461 ◽  
Author(s):  
Husam A. H. Al-Najjar ◽  
Bahareh Kalantar ◽  
Biswajeet Pradhan ◽  
Vahideh Saeidi ◽  
Alfian Abdul Halin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Training Data ◽  
Surface Model ◽  
Kappa Index ◽  
Diverse Range ◽  
Average Accuracy ◽  
Uav Images

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

scholarly journals Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG

2020 ◽  
Vol 378 ◽  
pp. 270-282 ◽  
Author(s):  
Mingxin Yu ◽  
Yichen Sun ◽  
Bofei Zhu ◽  
Lianqing Zhu ◽  
Yingzi Lin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Frequency Band ◽  
Pain Assessment ◽  
Cold Pain

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

scholarly journals Age estimation based on face images and pre-trained convolutional neural networks

2017 ◽  
Author(s):  
Abhinav Anand ◽  
Ruggero Donida Labati ◽  
Angelo Genovese ◽  
Enrique Munoz ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Face Images

Use of Convolutional Neural Networks in the Diagnosis of Corn Diseases

2021 ◽  
Author(s):  
Paulo Victor Cunha Lima ◽  
Edson Magalhães Costa ◽  
Maria Eliana da Silva Holanda ◽  
Dhian Kelson Leite Oliveira ◽  
Esley Teixeira Espírito Santo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Observation ◽  
Plant Diseases ◽  
Subjective Perception ◽  
Maize Crop ◽  
Production Chain ◽  
Average Accuracy ◽  
Proposed Model ◽  
Field Practice

The detection of corn (maize) crop diseases is traditionally carried out by farmers, based on their experience accumulated over a period of field practice. However, the visual observation may represent a risk of error due to subjective perception. This article presents an approach based on Deep Learning to identify diseases that affect corn crops. A public database with 3,852 images of maize plant leaves was used, dividedinto four classes: healthy corn, exserohilun leaf spot (northern leaf blight), common corn rust (common rust) and cercosporiosis (cercospora leaf/gray leaf). The proposed model used Convolutional Neural Networks (CNN) techniques for image classification. The four experiments indicated results with an average accuracy above 94.5%. These results in the identification and diagnosis of plant diseases can contribute significantly as atool to the improvement of the production chain that affect corn crops. All data are available at https://github.com/npcaufra/classificacao-doencas-milho .

scholarly journals Effective training of convolutional neural networks for age estimation based on knowledge distillation

2021 ◽  
Author(s):  
Antonio Greco ◽  
Alessia Saggese ◽  
Mario Vento ◽  
Vincenzo Vigilante
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Resource Constraints ◽  
Training Procedure ◽  
Face Images ◽  
Effective Training ◽  
Knowledge Distillation ◽  
Student Models ◽  
Teacher Model

AbstractAge estimation from face images can be profitably employed in several applications, ranging from digital signage to social robotics, from business intelligence to access control. Only in recent years, the advent of deep learning allowed for the design of extremely accurate methods based on convolutional neural networks (CNNs) that achieve a remarkable performance in various face analysis tasks. However, these networks are not always applicable in real scenarios, due to both time and resource constraints that the most accurate approaches often do not meet. Moreover, in case of age estimation, there is the lack of a large and reliably annotated dataset for training deep neural networks. Within this context, we propose in this paper an effective training procedure of CNNs for age estimation based on knowledge distillation, able to allow smaller and simpler “student” models to be trained to match the predictions of a larger “teacher” model. We experimentally show that such student models are able to almost reach the performance of the teacher, obtaining high accuracy over the LFW+, LAP 2016 and Adience datasets, but being up to 15 times faster. Furthermore, we evaluate the performance of the student models in the presence of image corruptions, and we demonstrate that some of them are even more resilient to these corruptions than the teacher model.

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