ArticleInternational journal of imaging systems and technology2022
A deep learning approach for classification of COVID and pneumonia using DenseNet-201.
Article in International journal of imaging systems and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Machine Learning-Assisted Classification of Pathogenic Yeasts Using Laser Light Scattering and Conventional Microscopy.Journal of imaging · 2026Article
- A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images.Scientific reports · 2026Article
- HybridCATNet: an explainable CNN-Transformer framework with early multi-channel fusion and uncertainty estimation for pneumonia detection from chest X-rays.Frontiers in digital health · 2026Article
- Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
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- Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.BMC infectious diseases · 2025Article
- Skin disease diagnosis using decision and feature level fusion of deep features.Frontiers in digital health · 2025Article
- Article
- SqueezeViX-Net with SOAE: A Prevailing Deep Learning Framework for Accurate Pneumonia Classification using X-Ray and CT Imaging Modalities.Current medical imaging · 2025Article
- A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking.PloS one · 2025Article
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- Review
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Efficient deep learning-based approach for malaria detection using red blood cell smears.Scientific reports · 2024Article
- Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction.IEEE journal of biomedical and health informatics · 2024Article
- Article
- Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of Monkeypox Images Using LIME-Enabled Investigation of Deep Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.
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