scholarly journals Prediction and Detection of COVID-19 from Chest X-Rays using Transfer Learning based Deep Convolutional Neural Networks

2021 ◽  
Author(s):  
Priyavrat Misra ◽  
Niranjan Panigrahi
Keyword(s):  
Transfer Learning ◽  
Performance Metrics ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Global Pandemic ◽  
Gradient Based ◽  
Digital Chest ◽  
Simulation And Evaluation ◽  
Detection Mechanisms ◽  
Limited Testing

Abstract With the ongoing outbreak of the COVID-19 global pandemic, the research community still struggles to develop early and reliable prediction and detection mechanisms for this infectious disease. The commonly used RT-PCR test is not readily available in areas with limited testing facilities, and it lags in performance and timeliness. This paper proposes a deep transfer learning-based approach to predict and detect COVID-19 from digital chest radiographs. In this study, three pre-trained convolutional neural network-based models (VGG16, ResNet18, and DenseNet121) have been fine tuned to detect COVID-19 infected patients from chest X-rays (CXRs). The most efficient model is further used to identify the affected regions using an unsupervised gradient-based localization technique. The proposed system uses a classification approach (normal vs. COVID-19 vs. pneumonia vs. lung opacity) using three supervised classification algorithms followed by gradient-based localization. The training, validation and testing of the system are performed using 21165 CXR images (10192 normal, 1345 pneumonia, 3616 COVID-19, and 6012 lung opacity). Simulation and evaluation results are presented using standard performance metrics, viz, accuracy, sensitivity, and specificity.

scholarly journals Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

2020 ◽  
Author(s):  
Sohaib Asif ◽  
Yi Wenhui ◽  
Hou Jin ◽  
Yi Tao ◽  
Si Jinhai
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Well Being ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Digital Chest ◽  
Normal Chest ◽  
Chest X Ray

AbstractThe COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID‐ 19 pneumonia patients using digital chest x‐ ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID‐ 19, 1345 viral pneumonia and 1341 normal chest x‐ ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.

scholarly journals Lyapunov-based Optimization of Edge Resources for Energy-Efficient Adaptive Federated Learning

2021 ◽  
Author(s):  
Claudio Battiloro ◽  
Paolo Di Lorenzo ◽  
Mattia Merluzzi ◽  
Sergio Barbarossa
Keyword(s):  
Wireless Network ◽  
Energy Efficient ◽  
Performance Metrics ◽  
Learning Task ◽  
Learning Performance ◽  
Deep Convolutional Neural Networks ◽  
Local Data ◽  
Data Driven Approach ◽  
Gradient Based ◽  
And Performance

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-based algorithms to perform continuous learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (e.g., set of transmitting devices, transmit powers, bits, and rates) and computation resources (e.g., CPU cycles at devices and at server) in order to strike the best trade-off between power, latency, and performance of the federated learning task. The framework admits both a model-based implementation, where the learning performance metrics are available in closed-form, and a data-driven approach, which works with online estimates of the learning performance of interest. The method is then customized to the case of federated least mean squares (LMS) estimation, and federated training of deep convolutional neural networks. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, adaptive federated learning at the wireless network edge.

scholarly journals Lyapunov-based Optimization of Edge Resources for Energy-Efficient Adaptive Federated Learning

2021 ◽  
Author(s):  
Claudio Battiloro ◽  
Paolo Di Lorenzo ◽  
Mattia Merluzzi ◽  
Sergio Barbarossa
Keyword(s):  
Wireless Network ◽  
Energy Efficient ◽  
Performance Metrics ◽  
Learning Task ◽  
Learning Performance ◽  
Deep Convolutional Neural Networks ◽  
Local Data ◽  
Data Driven Approach ◽  
Gradient Based ◽  
And Performance

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-based algorithms to perform continuous learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (e.g., set of transmitting devices, transmit powers, bits, and rates) and computation resources (e.g., CPU cycles at devices and at server) in order to strike the best trade-off between power, latency, and performance of the federated learning task. The framework admits both a model-based implementation, where the learning performance metrics are available in closed-form, and a data-driven approach, which works with online estimates of the learning performance of interest. The method is then customized to the case of federated least mean squares (LMS) estimation, and federated training of deep convolutional neural networks. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, adaptive federated learning at the wireless network edge.

scholarly journals Deep Learning Classification of Active Tuberculosis Using Chest X-Rays: Efficacy of Transfer Learning and Generalization Performance of Cross-Population Datasets

2022 ◽  
Author(s):  
James Devasia ◽  
Hridyanand Goswami ◽  
Subitha Lakshminarayanan ◽  
Manju Rajaram ◽  
Subathra Adithan ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Transfer Learning ◽  
Model Performance ◽  
Active Tuberculosis ◽  
Radiology Workflow ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Significant Drop ◽  
Test Set ◽  
External Test

Abstract Chest X-ray based diagnosis of active Tuberculosis (TB) is one of the oldest ubiquitous tests in medical practice. Artificial Intelligence (AI) based automated detection of abnormality in chest radiography is crucial in radiology workflow. Most deep convolutional neural networks (DCNN) for diagnosing TB by transfer learning from natural images and using the same dataset to evaluate the model performance and diagnostic accuracy. However, dataset shift is a known issue in predictive models in AI, which is unexplored. In this work, we fine-tuned, validated, and tested two benchmark architectures and utilized the transfer learning methodology to measure the diagnostic accuracy on cross-population datasets. We achieved remarkable calcification accuracy of 100% and area under the receiver operating characteristic (AUC) 1.000 [1.000 – 1.000] (with a sensitivity 0.985 [0.971 – 1.000] and a specificity of 0.986 [0.971 – 1.000]) on intramural test set, but significant drop in extramural test set. Accuracy on various extramural test sets varies 50% - 70%, AUC ranges 0.527 – 0.865 (sensitivity and specificity fluctuate 0.394 – 0.995 and 0.443 – 0.864 respectively). Diagnostic performance on the intramural test set observed in this study shows that DCNN can accurately classify active TB and normal chest radiographs, however the external test set shows DCNN is less likely to generalize well on models trained on specific population dataset.

scholarly journals Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4520
Author(s):  
Luis Lopes Chambino ◽  
José Silvestre Silva ◽  
Alexandre Bernardino
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Facial Recognition ◽  
Spectral Band ◽  
Support Vector ◽  
Multispectral Images ◽  
Forgery Detection ◽  
K Nearest Neighbor ◽  
Deep Convolutional Neural Networks ◽  
Domain Specific

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.

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