ArticleNeural computing & applications2021
Detection of COVID-19 from CT scan images: A spiking neural network-based approach.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.Neural computing & applications · 2023Article
- A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images.Journal of ambient intelligence and humanized computing · 2023Article
- Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications.Journal of imaging · 2022Review
- Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT.Expert systems with applications · 2022Article
- Novel deep learning approach to model and predict the spread of COVID-19.Intelligent systems with applications · 2022Article
- COVID-19 detection from CT scans using a two-stage framework.Expert systems with applications · 2022Article
- COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation.Computers & graphics · 2022Article
- VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm.Neural computing & applications · 2022Article
- ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images.Multimedia tools and applications · 2022Article
- Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging.Frontiers in medicine · 2022Article
- Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection.Computers in biology and medicine · 2021Article
- An ensemble learning approach to digital corona virus preliminary screening from cough sounds.Scientific reports · 2021Article
- Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans.Scientific reports · 2021Article
- Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.
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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.