scholarly journals A deep learning ensemble approach to prioritize candidate drugs against novel coronavirus 2019-nCoV/SARS-CoV-2

2021 ◽  
pp. 107945
Author(s):  
Deepthi K. ◽  
Jereesh A.S. ◽  
Yuansheng Liu
Keyword(s):  
Deep Learning ◽  
Ensemble Approach ◽  
Novel Coronavirus

scholarly journals A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30551-30572
Author(s):  
Md. Milon Islam ◽  
Fakhri Karray ◽  
Reda Alhajj ◽  
Jia Zeng
Keyword(s):  
Deep Learning ◽  
Learning Techniques ◽  
Novel Coronavirus

Deep learning based ensemble approach for probabilistic wind power forecasting

2017 ◽  
Vol 188 ◽  
pp. 56-70 ◽  
Author(s):  
Huai-zhi Wang ◽  
Gang-qiang Li ◽  
Gui-bin Wang ◽  
Jian-chun Peng ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Ensemble Approach ◽  
Power Forecasting

scholarly journals Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study

2020 ◽  
Vol 128 ◽  
pp. 109041 ◽  
Author(s):  
Xiangjun Wu ◽  
Hui Hui ◽  
Meng Niu ◽  
Liang Li ◽  
Li Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multicentre Study ◽  
Fusion Model ◽  
Novel Coronavirus

scholarly journals A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150530-150539 ◽  
Author(s):  
Sehrish Qummar ◽  
Fiaz Gul Khan ◽  
Sajid Shah ◽  
Ahmad Khan ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Ensemble Approach

scholarly journals A Deep-Learning-Based Framework for Automated Diagnosis of COVID-19 Using X-ray Images

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 419 ◽  
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam
Keyword(s):  
Deep Learning ◽  
False Negative ◽  
False Negative Rate ◽  
Complex Nature ◽  
Learning Models ◽  
Daily Lives ◽  
X Ray ◽  
High False Negative Rate ◽  
Diagnosis Method ◽  
Novel Coronavirus

The emergence and outbreak of the novel coronavirus (COVID-19) had a devasting effect on global health, the economy, and individuals’ daily lives. Timely diagnosis of COVID-19 is a crucial task, as it reduces the risk of pandemic spread, and early treatment will save patients’ life. Due to the time-consuming, complex nature, and high false-negative rate of the gold-standard RT-PCR test used for the diagnosis of COVID-19, the need for an additional diagnosis method has increased. Studies have proved the significance of X-ray images for the diagnosis of COVID-19. The dissemination of deep-learning techniques on X-ray images can automate the diagnosis process and serve as an assistive tool for radiologists. In this study, we used four deep-learning models—DenseNet121, ResNet50, VGG16, and VGG19—using the transfer-learning concept for the diagnosis of X-ray images as COVID-19 or normal. In the proposed study, VGG16 and VGG19 outperformed the other two deep-learning models. The study achieved an overall classification accuracy of 99.3%.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 403-415
Author(s):  
Abeer Badawi ◽  
Khalid Elgazzar
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Healthcare Providers ◽  
Large Set ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

scholarly journals Forecasting the Number of Infections of Novel Coronavirus with Deep Learning

2020 ◽  
Vol 176 (20) ◽  
pp. 21-24
Author(s):  
Vakada Naveen ◽  
Chunduri Aasish ◽  
Manne Kavya ◽  
Meda Vidhyalakshmi
Keyword(s):  
Deep Learning ◽  
Novel Coronavirus

scholarly journals Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoshuo Li ◽  
Wenjun Tan ◽  
Pan Liu ◽  
Qinghua Zhou ◽  
Jinzhu Yang
Keyword(s):  
Deep Learning ◽  
Medical Personnel ◽  
Classification Performance ◽  
Cascade Classifier ◽  
Data Set ◽  
Global Pandemic ◽  
Good Classification Performance ◽  
Ct Image Analysis ◽  
Assisted Diagnosis ◽  
Novel Coronavirus

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.

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