COVID-GATNet: A Deep Learning Framework for Screening of COVID-19 from Chest X-Ray Images

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

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DenseCapsNet: Detection of COVID-19 X-ray Images Using a Capsule Network

10.21203/rs.3.rs-84220/v1 ◽

2020 ◽

Author(s):

Hao Quan ◽

Xiaosong Xu ◽

Tingting Zheng ◽

Zhi Li ◽

Mingfang Zhao ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Data Augmentation ◽

Detection Performance ◽

Convolution Neural Network ◽

X Ray ◽

Learning Framework ◽

X Ray Imaging ◽

Chest X Ray ◽

Novel Coronavirus

Abstract Objective: A deep learning framework for detecting COVID-19 is developed, and a small amount of chest X-ray data is used to accurately screen COVID-19.Methods: In this paper, we propose a deep learning framework that integrates convolution neural network and capsule network. DenseNet and CapsNet fusion are used to give full play to their respective advantages, reduce the dependence of convolution neural network on a large amount of data, and can quickly and accurately distinguish COVID-19 from Non-COVID-19 through chest X-ray imaging.Results: A total of 1472 chest X-ray COVID-19 and non-COVID-19 images are used, this method can achieve an accuracy of 99.32% and a precision of 100%, with 98.55% sensitivity and 100% specificity.Conclusion: These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia X-ray detection. We also prove through experiments that the detection performance of DenseCapsNet is not affected fundamentally by a lack of data augmentation and pre-training.

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CoWarriorNet: A Novel Deep-Learning Framework for CoVID-19 Detection from Chest X-Ray Images

New Generation Computing ◽

10.1007/s00354-021-00143-1 ◽

2021 ◽

Author(s):

Indrani Roy ◽

Rinita Shai ◽

Arijit Ghosh ◽

Anirban Bej ◽

Soumen Kumar Pati

Keyword(s):

Deep Learning ◽

X Ray ◽

Learning Framework ◽

Chest X Ray

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Deep Learning for COVID-19 Recognition

10.20944/preprints202105.0711.v1 ◽

2021 ◽

Author(s):

Yuchen Zhang ◽

Yanyan Zhang

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Cause Of Death ◽

X Ray ◽

Learning Framework ◽

Residual Learning ◽

Epidemic Period ◽

Chest X Ray ◽

New Type

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

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Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images

Pattern Recognition Letters ◽

10.1016/j.patrec.2019.11.013 ◽

2020 ◽

Vol 129 ◽

pp. 271-278 ◽

Cited By ~ 33

Author(s):

Abhir Bhandary ◽

G. Ananth Prabhu ◽

V. Rajinikanth ◽

K. Palani Thanaraj ◽

Suresh Chandra Satapathy ◽

...

Keyword(s):

Deep Learning ◽

Ct Scan ◽

X Ray ◽

Learning Framework ◽

Chest X Ray ◽

Lung Ct ◽

Lung Ct Scan

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Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Diagnostics ◽

10.3390/diagnostics10090649 ◽

2020 ◽

Vol 10 (9) ◽

pp. 649 ◽

Cited By ~ 1

Author(s):

Nada M. Elshennawy ◽

Dina M. Ibrahim

Keyword(s):

Deep Learning ◽

Performance Metrics ◽

Short Term Memory ◽

Area Under The Curve ◽

The Elderly ◽

Learning Models ◽

X Ray ◽

Learning Framework ◽

Chest X Ray ◽

Long Short Term Memory

Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models—MobileNetV2, CNN, and LSTM-CNN—achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.

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