Automated detection and forecasting of covid-19 using deep learning techniques: A review
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning
Semi-supervised learning on class-imbalanced data, although a realistic problem, has been
under studied. While existing semi-supervised learning (SSL) methods are known to perform …
under studied. While existing semi-supervised learning (SSL) methods are known to perform …
CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR
To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is
crucial to have an effective screening of infected patients to be isolated and treated. Chest X …
crucial to have an effective screening of infected patients to be isolated and treated. Chest X …
Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated …
G Celik - Applied Soft Computing, 2023 - Elsevier
Covid-19 has become a worldwide epidemic which has caused the death of millions in a
very short time. This disease, which is transmitted rapidly, has mutated and different …
very short time. This disease, which is transmitted rapidly, has mutated and different …
A comprehensive review of deep learning-based methods for COVID-19 detection using chest X-ray images
SS Alahmari, B Altazi, J Hwang, S Hawkins… - Ieee …, 2022 - ieeexplore.ieee.org
The novel coronavirus disease 2019 (COVID-19) added tremendous pressure on healthcare
services worldwide. COVID-19 early detection is of the utmost importance to control the …
services worldwide. COVID-19 early detection is of the utmost importance to control the …
Smoothed adaptive weighting for imbalanced semi-supervised learning: Improve reliability against unknown distribution data
Despite recent promising results on semi-supervised learning (SSL), data imbalance,
particularly in the unlabeled dataset, could significantly impact the training performance of a …
particularly in the unlabeled dataset, could significantly impact the training performance of a …
[Retracted] Lung Disease Classification in CXR Images Using Hybrid Inception‐ResNet‐v2 Model and Edge Computing
CM Sharma, L Goyal, VM Chariar… - Journal of Healthcare …, 2022 - Wiley Online Library
Chest X‐ray (CXR) imaging is one of the most widely used and economical tests to
diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge …
diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge …
Improving uncertainty estimation with semi-supervised deep learning for COVID-19 detection using chest X-ray images
In this work we implement a COVID-19 infection detection system based on chest X-ray
images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer …
images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer …
Explainable machine learning for COVID-19 pneumonia classification with texture-based features extraction in chest radiography
Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the
coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a …
coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a …
Image entropy equalization: A novel preprocessing technique for image recognition tasks
Image entropy is the metric used to represent a complexity of an image. This study considers
the hypothesis that image entropy differences affect machine learning algorithms' …
the hypothesis that image entropy differences affect machine learning algorithms' …