ArticleSensors (Basel, Switzerland)2021
Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Light-XAI: a CADx for explainable cervical cancer detection via attention-based lightweight convolutional neural networks and layer-wise feature fusion.BioData mining · 2026Article
- Efficient and explainable histopathology for cancer detection using dual-teacher distillation and integrated gradients.Frontiers in medicine · 2026Article
- Cgc-YOLO: A New Detection Model for Defect Detection of Tea Tree Seeds.Sensors (Basel, Switzerland) · 2025Article
- IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.Bioengineering (Basel, Switzerland) · 2025Article
- Rethinking Domain-Specific Pretraining by Supervised or Self-Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold-Start Active Learning.Health care science · 2025Article
- Automated classification of chest X-rays: a deep learning approach with attention mechanisms.BMC medical imaging · 2025Article
- Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning.Frontiers in computational neuroscience · 2025Article
- OculusNet: Detection of retinal diseases using a tailored web-deployed neural network and saliency maps for explainable AI.Frontiers in medicine · 2025Article
- The two-stage detection-after-segmentation model improves the accuracy of identifying subdiaphragmatic lesions.Scientific reports · 2024Article
- Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Enhancing surgical decision-making in NEC with ResNet18: a deep learning approach to predict the need for surgery through x-ray image analysis.Frontiers in pediatrics · 2024Article
- A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning.Sensors (Basel, Switzerland) · 2023Article
- Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets.Bioengineering (Basel, Switzerland) · 2023Article
- 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
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- Article
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.
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.