ArticleApplied soft computing2022
A deep fuzzy model for diagnosis of COVID-19 from CT images.
Article in Applied soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 38 citations in OpenAlex.
- A radiology-aware fuzzy deep learning framework with entropy-guided feature selection for robust multi-disease chest X-ray classification across multiple magnifications.Radiological physics and technology · 2026Article
- A Flexible Multi-Channel Deep Network Leveraging Texture and Spatial Features for Diagnosing New COVID-19 Variants in Lung CT Scans.Tomography (Ann Arbor, Mich.) · 2025Article
- COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.Heliyon · 2024Article
- Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.Multimedia tools and applications · 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
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation.Frontiers in microbiology · 2023Article
- A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection.Soft computing · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Therefore, this paper constructs a fuzzy classifier, which aims to detect infected subjects by observing and analyzing the CT images of suspected patients. Firstly, a deep learning algorithm is used to extract the low-level features of CT images in the COVID-CT dataset. Subsequently, we analyze the extracted feature information with attribute reduction algorithm to obtain features with high recognition. Then, some key features are selected as the input for the fuzzy diagnosis model to the training model. Finally, several images in the dataset are used as the test set to test the trained fuzzy classifier. The obtained accuracy rate is 94.2%, and the F1-score is 93.8%. Experimental results show that, compared with the deep learning diagnosis methods widely used in medical image analysis, the proposed fuzzy model improves the accuracy and efficiency of diagnosis, which consequently helps to curb the spread of COVID-19.
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