ArticleComputers in biology and medicine2021
COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.Discover oncology · 2025Article
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder.Health information science and systems · 2024Article
- A GPT-based EHR modeling system for unsupervised novel disease detection.Journal of biomedical informatics · 2024Article
- Morphological classification of neurons based on Sugeno fuzzy integration and multi-classifier fusion.Scientific reports · 2024Article
- A multistage framework for respiratory disease detection and assessing severity in chest X-ray images.Scientific reports · 2024Article
- Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A 3D lung lesion variational autoencoder.Cell reports methods · 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
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning.Journal of medical and biological engineering · 2023Article
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- COVID-19 chest X-ray detection through blending ensemble of CNN snapshots.Biomedical signal processing and control · 2022Article
- An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-19 detection from CT scans using a two-stage framework.Expert systems with applications · 2022Article
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4 authors.
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Abstract
The COVID-19 pandemic has collapsed the public healthcare systems, along with severely damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus, led to community spread, causing the death of more than a million people worldwide. The primary reason for the uncontrolled spread of the virus is the lack of provision for population-wise screening. The apparatus for RT-PCR based COVID-19 detection is scarce and the testing process takes 6-9 h. The test is also not satisfactorily sensitive (71% sensitive only). Hence, Computer-Aided Detection techniques based on deep learning methods can be used in such a scenario using other modalities like chest CT-scan images for more accurate and sensitive screening. In this paper, we propose a method that uses a Sugeno fuzzy integral ensemble of four pre-trained deep learning models, namely, VGG-11, GoogLeNet, SqueezeNet v1.1 and Wide ResNet-50-2, for classification of chest CT-scan images into COVID and Non-COVID categories. The proposed framework has been tested on a publicly available dataset for evaluation and it achieves 98.93% accuracy and 98.93% sensitivity on the same. The model outperforms state-of-the-art methods on the same dataset and proves to be a reliable COVID-19 detector. The relevant source codes for the proposed approach can be found at: https://github.com/Rohit-Kundu/Fuzzy-Integral-Covid-Detection.
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