ArticleScientific reports2021
Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 101 citations in OpenAlex.
- A Bayesian-Optimized Weighted Fuzzy Rank-Based Ensemble for Coronary Stenosis Detection in X-ray Angiography.Journal of imaging informatics in medicine · 2026Article
- Modeling Postoperative Nerve Regeneration Using Diffusion MRI: A Preclinical Study of a Novel Mathematical Approach.Muscle & nerve · 2026Article
- Identifying Disinformation on the Extended Impacts of COVID-19: Methodological Investigation Using a Fuzzy Ranking Ensemble of Natural Language Processing Models.Journal of medical Internet research · 2025Article
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- Stage-based colorectal cancer prediction on uncertain dataset using rough computing and LSTM models.Scientific reports · 2024Article
- Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model.Journal of imaging informatics in medicine · 2024Article
- Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.BMC medical imaging · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.Scientific reports · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme.PloS one · 2023Article
- A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.Applied soft computing · 2022Article
- COVID-19 chest X-ray detection through blending ensemble of CNN snapshots.Biomedical signal processing and control · 2022Article
- A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain.Computers in biology and medicine · 2022Article
- Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data.Healthcare (Basel, Switzerland) · 2022Article
- Classifier Fusion for Detection of COVID-19 from CT Scans.Circuits, systems, and signal processing · 2022Article
- COFE-Net: An ensemble strategy for Computer-Aided Detection for COVID-19.Measurement : journal of the International Measurement Confederation · 2022Article
- Improved COVID-19 detection with chest x-ray images using deep learning.Multimedia tools and applications · 2022Article
- MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
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