ArticleMultimedia tools and applications2022
Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 72 citations in OpenAlex.
- A cloud-edge adaptive lightweight network with dynamic inference enables real-time ultrasound image segmentation.Scientific reports · 2026Article
- Enhancing depression diagnosis with augmented brain signal driven decorrelated graph neural networks.Communications medicine · 2026Article
- U-FDL-PPE: a unified federated deep learning framework with privacy-preserving explainability for early and accurate viral disease prediction.Frontiers in radiology · 2025Article
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives.Sensors (Basel, Switzerland) · 2023Review
- A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection.Expert systems with applications · 2022Article
- Machine learning and deep learning approach for medical image analysis: diagnosis to detection.Multimedia tools and applications · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
- Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT.Expert systems with applications · 2022Article
- Towards effective offloading mechanisms in fog computing.Multimedia tools and applications · 2022Article
- Diabetic Macular Edema Detection Using End-to-End Deep Fusion Model and Anatomical Landmark Visualization on an Edge Computing Device.Frontiers in medicine · 2022Article
- A cad system design based on HybridMultiscale convolutional Mantaray network for pneumonia diagnosis.Multimedia tools and applications · 2022Article
- COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The novel coronavirus outbreak has spread worldwide, causing respiratory infections in humans, leading to a huge global pandemic COVID-19. According to World Health Organization, the only way to curb this spread is by increasing the testing and isolating the infected. Meanwhile, the clinical testing currently being followed is not easily accessible and requires much time to give the results. In this scenario, remote diagnostic systems could become a handy solution. Some existing studies leverage the deep learning approach to provide an effective alternative to clinical diagnostic techniques. However, it is difficult to use such complex networks in resource constraint environments. To address this problem, we developed a fine-tuned deep learning model inspired by the architecture of the MobileNet V2 model. Moreover, the developed model is further optimized in terms of its size and complexity to make it compatible with mobile and edge devices. The results of extensive experimentation performed on a real-world dataset consisting of 2482 chest Computerized Tomography scan images strongly suggest the superiority of the developed fine-tuned deep learning model in terms of high accuracy and faster diagnosis time. The proposed model achieved a classification accuracy of 96.40%, with approximately ten times shorter response time than prevailing deep learning models. Further, McNemar's statistical test results also prove the efficacy of the proposed model.
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Registered trials
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.