ArticleBiomedical signal processing and control2022
COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.Annals of biomedical engineering · 2026Pooled it
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.Journal of imaging informatics in medicine · 2024Article
- COVID‑19 detection from chest X-ray images using transfer learning.Scientific reports · 2024Article
- Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images.Frontiers in medicine · 2024Article
- A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.Journal of digital imaging · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images.Biomedical signal processing and control · 2023Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- Deep learning models-based CT-scan image classification for automated screening of COVID-19.Biomedical signal processing and control · 2023Article
- Coronavirus disease identification using Multi-subband feature analysis in DWT domain.Procedia computer science · 2023Article
- 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 convolutions neural network.Applied soft computing · 2023Article
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- X-ray image based COVID-19 detection using evolutionary deep learning approach.Expert systems with applications · 2022Article
- LHSPred: A web based application for predicting lung health severity.Biomedical signal processing and control · 2022Article
- DCML: Deep contrastive mutual learning for COVID-19 recognition.Biomedical signal processing and control · 2022Article
- Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform.Biocybernetics and biomedical engineeringArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the current scenario, novel coronavirus disease (COVID-19) spread is increasing day-by-day. It is very important to control and cure this disease. Reverse transcription-polymerase chain reaction (RT-PCR), chest computerized tomography (CT) imaging options are available as a significantly useful and more truthful tool to classify COVID-19 within the epidemic region. Most of the hospitals have CT imaging machines. It will be fruitful to utilize the chest CT images for early diagnosis and classification of COVID-19 patients. This requires a radiology expert and a good amount of time to classify the chest CT-based COVID-19 images especially when the disease is spreading at a rapid rate. During this pandemic COVID-19, there is a need for an efficient automated way to check for infection. CT is one of the best ways to detect infection inpatients. This paper introduces a new method for preprocessing and classifying COVID-19 positive and negative from CT scan images. The method which is being proposed uses the concept of empirical wavelet transformation for preprocessing, selecting the best components of the red, green, and blue channels of the image are trained on the proposed network. With the proposed methodology, the classification accuracy of 85.5%, F1 score of 85.28%, and AUC of 96.6% are achieved.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.