COVID-19 X-ray Image Diagnosis Using Deep Convolutional Neural Networks

2021 ◽  
pp. 733-741
Author(s):  
Alisa Kunapinun ◽  
Matthew N. Dailey
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Image Diagnosis

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 COVID-19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm-Based Feature Selection from X-Ray Images

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mohammad Saber Iraji ◽  
Mohammad-Reza Feizi-Derakhshi ◽  
Jafar Tanha
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Approach ◽  
Svm Classifier ◽  
Metaheuristic Algorithm ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Human Lungs ◽  
Differential Algorithm ◽  
Numerous People

The new COVID-19 is rapidly spreading and has already claimed the lives of numerous people. The virus is highly destructive to the human lungs, and early detection is critical. As a result, this paper presents a hybrid approach based on deep convolutional neural networks that are very effective tools for image classification. The feature vectors were extracted from the images using a deep convolutional neural network, and the binary differential metaheuristic algorithm was used to select the most valuable features. The SVM classifier was then given these optimized features. For the study, a database containing images from three categories, including COVID-19, pneumonia, and a healthy category, included 1092 X-ray samples, was used. The proposed method achieved a 99.43% accuracy, a 99.16% sensitivity, and a 99.57% specificity. Our findings indicate that the proposed method outperformed recent studies on COVID-19 detection using 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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