ArticleInformatics in medicine unlocked2022
Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images.
Article in Informatics in medicine unlocked, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Article
- Dynamic ultrasound motion metrics combined with deep learning for clinical differentiation of subacromial impingement syndrome.La Radiologia medica · 2026Article
- A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD.PloS one · 2026Article
- A robust privacy-preserving federated framework for kidney CT image classification using transfer learning models.Frontiers in artificial intelligence · 2026Article
- Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.Scientific data · 2025Article
- (KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network.Frontiers in big data · 2025Article
- Hybrid ResNet-ViT Framework for Endometrial Lesion Analysis: A Comparative Study of MRI and CT in Endometrial Cancer Classification.International journal of women's health · 2025Article
- Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods.Tomography (Ann Arbor, Mich.) · 2024Article
- Automatic classification of fungal-fungal interactions using deep leaning models.Computational and structural biotechnology journal · 2024Article
- A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection.Sensors (Basel, Switzerland) · 2024Article
- A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart.BMC gastroenterology · 2024Article
- Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks.Scientific reports · 2024Article
- Offloading the computational complexity of transfer learning with generic features.PeerJ. Computer science · 2024Article
- Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.Behavioural neurology · 2024Article
- Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection.Scientific reports · 2023Article
- Article
- Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Classical-Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays.Journal of imaging · 2023Article
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 2023Article
- Self-supervised learning for medical image classification: a systematic review and implementation guidelines.NPJ digital medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 cases are putting pressure on healthcare systems all around the world. Due to the lack of available testing kits, it is impractical for screening every patient with a respiratory ailment using traditional methods (RT-PCR). In addition, the tests have a high turn-around time and low sensitivity. Detecting suspected COVID-19 infections from the chest X-ray might help isolate high-risk people before the RT-PCR test. Most healthcare systems already have X-ray equipment, and because most current X-ray systems have already been computerized, there is no need to transfer the samples. The use of a chest X-ray to prioritize the selection of patients for subsequent RT-PCR testing is the motivation of this work. Transfer learning (TL) with fine-tuning on deep convolutional neural network-based ResNet50 model has been proposed in this work to classify COVID-19 patients from the COVID-19 Radiography Database. Ten distinct pre-trained weights, trained on varieties of large-scale datasets using various approaches such as supervised learning, self-supervised learning, and others, have been utilized in this work. Our proposed
Indexed as
Identifiers
What OpenQuestion holds
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.