Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.
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TL;DR: This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection and evaluates the effectiveness of eight pre-trained Convolutional Neural Network models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, Res net-50 and Inception-V3 for classification of CO VID-19 from normal cases.
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About: This article is published in Biomedical Signal Processing and Control. The article was published on 01 Feb 2021. and is currently open access. The article focuses on the topics: Deep learning.
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Citations
Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification
Hongfei Sun,Ge Ren,Xinzhi Teng,Liming Song,Kang Li,Jiayi Yang,Xiaofei Hu,Yue Zhang,Shiu Bun Nelson Wan,Man Fung Esther Wong,King Kwong Chan,Hoi Ching Hailey Tsang,Lu Xu,Tak Chiu Wu,Feng Ming Kong,Yi-Xiang J. Wang,Jing Qin,Wing Chi Lawrence Chan,Michael Ying,Jing Cai +19 more
TL;DR: In this paper , an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images was developed to improve the accuracy of COVID-19 classification.
4
Deep-COVID: Detection and Analysis of COVID-19 Outcomes Using Deep Learning
M. Ibrahim Khalil,Saif Ur Rehman,Mousa Alhajlah,Awais Mahmood,Tehmina Karamat,Muhammad Haneef,Ashwaq Alhajlah +6 more
TL;DR: In this article , the authors presented a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset.
Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting
Wilson Bakasa,Serestina Viriri +1 more
TL;DR: It is concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images.
4
Novel Square Error Minimization-Based Multilevel Thresholding Method for COVID-19 X-Ray Image Analysis Using Fast Cuckoo Search
TL;DR: A novel non-entropic threshold selection method is proposed, which is the primary key contribution having found a new source of information to the biomedical image processing field, and the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed.
3
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
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