scholarly journals COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach

2021 ◽  
Vol 6 (1) ◽  
pp. 1-3
Author(s):  
Hala As'ad ◽  
Hilda Azmi ◽  
Pengcheng Xi ◽  
Ashkan Ebadi ◽  
Stéphane Tremblay ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Weighted Average ◽  
Learning Approach ◽  
Imbalanced Dataset ◽  
Deep Convolutional Neural Networks ◽  
Model Sensitivity ◽  
X Ray ◽  
Chest X Ray

COVID-19 pandemic has drastically changed our lives. Chest radiographyhas been used to detect COVID-19. However, the numberof publicly available COVID-19 x-ray images is extremely limited,resulting in a highly imbalanced dataset. This is a challenge whenusing deep learning for classification and detection. In this work, wepropose the use of pre-trained deep Convolutional Neural Networks(CNN) and integrate them with a few-shot learning approach namedimprinted weights. The integrated model is fine tuned to enhancethe capability of detecting COVID-19. The proposed solution thencombines the fine-tuned models using a weighted average ensemblefor achieving an optimal 82% sensitivity to COVID-19. To thebest of authors’ knowledge, the proposed solution is one of the firstto utilize imprinted weights model with weighted average ensemblefor enhancing the model sensitivity to COVID-19.

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks

2019 ◽  
Vol 38 (5) ◽  
pp. 1197-1206 ◽  
Author(s):  
Hojjat Salehinejad ◽  
Errol Colak ◽  
Tim Dowdell ◽  
Joseph Barfett ◽  
Shahrokh Valaee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Chest X Ray

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

scholarly journals Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 545 ◽  
Author(s):  
Hsin-Jui Chen ◽  
Shanq-Jang Ruan ◽  
Sha-Wo Huang ◽  
Yan-Tsung Peng
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Processing Approach ◽  
Segmentation Accuracy ◽  
Enhancement Method ◽  
Digital Chest ◽  
Chest X Ray ◽  
Model Training

Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is important in computer-aided diagnosis. In this paper, we propose an adaptive pre-processing approach for segmenting the lung regions from CXR images using convolutional neural networks-based (CNN-based) architectures. It is comprised of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Second, adaptive image binarization is applied to CXR images to separate the image foreground and background. Third, CNN-based architectures are trained on the binarized images for image segmentation. The experimental results show that the proposed pre-processing approach is applicable and effective to various CNN-based architectures and can achieve comparable segmentation accuracy to that of state-of-the-art methods while greatly expediting the model training by up to 20.74 % and reducing storage space for CRX image datasets by down to 94.6 % on average.

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