ArticleSoft computing2023
A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection.
Article in Soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification.Heliyon · 2025Article
- Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.Cancers · 2025Article
- PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images.Medical & biological engineering & computing · 2024Article
- Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.BMC medical informatics and decision making · 2024Article
- A tree-based explainable AI model for early detection of Covid-19 using physiological data.BMC medical informatics and decision making · 2024Article
- COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.Heliyon · 2024Article
- Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation.Heliyon · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- A fine-tuned YOLOv5 deep learning approach for real-time house number detection.PeerJ. Computer science · 2023Article
Corrections and comments
- Retraction · 2024-08-07Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Compromised Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Rogue Editor · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.
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