Role of artificial intelligence in COVID-19 detection
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and
affected the livelihood of many more people. Early and rapid detection of COVID-19 is a …
affected the livelihood of many more people. Early and rapid detection of COVID-19 is a …
Unsupervised domain adaptation for Covid-19 classification based on balanced slice Wasserstein distance
J Gu, X Qian, Q Zhang, H Zhang, F Wu - Computers in Biology and …, 2023 - Elsevier
Covid-19 has swept the world since 2020, taking millions of lives. In order to seek a rapid
diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been …
diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been …
[HTML][HTML] PDAtt-Unet: Pyramid dual-decoder attention Unet for Covid-19 infection segmentation from CT-scans
Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …
Segmentation and classification on chest radiography: a systematic survey
Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders.
A trained radiologist is required for interpreting the radiographs. But sometimes, even …
A trained radiologist is required for interpreting the radiographs. But sometimes, even …
[HTML][HTML] A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest
Abstract Several Artificial Intelligence-based models have been developed for COVID-19
disease diagnosis. In spite of the promise of artificial intelligence, there are very few models …
disease diagnosis. In spite of the promise of artificial intelligence, there are very few models …
[HTML][HTML] SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images
COVID-19 is a rapidly spreading viral disease and has affected over 100 countries
worldwide. The numbers of casualties and cases of infection have escalated particularly in …
worldwide. The numbers of casualties and cases of infection have escalated particularly in …
Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes
One of the primary challenges in applying deep learning approaches to medical imaging is
the limited availability of data due to various factors. These factors include concerns about …
the limited availability of data due to various factors. These factors include concerns about …
Analyzing the effect of filtering and feature-extraction techniques in a machine learning model for identification of infectious disease using radiography imaging
J Rasheed - Symmetry, 2022 - mdpi.com
The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has
facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of …
facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of …
[HTML][HTML] Deep learning techniques for hyperspectral image analysis in agriculture: A review
In recent years, there has been a growing emphasis on assessing and ensuring the quality
of horticultural and agricultural produce. Traditional methods involving field measurements …
of horticultural and agricultural produce. Traditional methods involving field measurements …
Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images
Computed tomography (CT) scans, or radiographic images, were used to aid in the early
diagnosis of patients and detect normal and abnormal lung function in the human chest …
diagnosis of patients and detect normal and abnormal lung function in the human chest …