ArticleEURASIP journal on advances in signal processing2021
A comparative study of multiple neural network for detection of COVID-19 on chest X-ray.
Article in EURASIP journal on advances in signal processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
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- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.Frontiers in artificial intelligence · 2025Article
- X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).Journal of imaging · 2024Article
- Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning.Scientific reports · 2024Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Pollen Grain Classification Using Some Convolutional Neural Network Architectures.Journal of imaging · 2024Article
- SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.Acta medica Philippina · 2024Article
- Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection.Interdisciplinary sciences, computational life sciences · 2023Article
- Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning.Journal of cancer research and clinical oncology · 2023Article
- PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.Medical & biological engineering & computing · 2023Article
- A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images.Journal of advanced research · 2023Article
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- Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images.Biomedicines · 2022Article
- COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave.Frontiers in public health · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.
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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.