ArticleBiomedical signal processing and control2022
A multi model ensemble based deep convolution neural network structure for detection of COVID19.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.PLOS digital health · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- White blood cell classification using custom deep neural network and visualizing features of the images using heatmaps.Scientific reports · 2026Article
- Parameter-Reduced YOLOv8n with GhostConv and C3Ghost for Automated Blood Cell Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Attention-Based Dual-Path Deep Learning for Blood Cell Image Classification Using ConvNeXt and Swin Transformer.Journal of imaging informatics in medicine · 2026Article
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Design and implementation of a low-cost gimbal-based angular ultrasound gantry for optimal tissue slice selection using deep learning.HardwareX · 2025Article
- An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2024Article
- Adversarial training collaborating hybrid convolution-transformer network for automatic identification of reactive lymphocytes in peripheral blood.Biomedical optics express · 2024Article
- A deep convolutional neural network approach using medical image classification.BMC medical informatics and decision making · 2024Article
- Generalizable disease detection using model ensemble on chest X-ray images.Scientific reports · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- White blood cells classification using multi-fold pre-processing and optimized CNN model.Scientific reports · 2024Article
- Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability.Scientific reports · 2023Article
- Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images.Heliyon · 2023Article
- Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation.Pattern recognition · 2023Article
- A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images.Diagnostics (Basel, Switzerland) · 2022Review
- White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.Healthcare (Basel, Switzerland) · 2022Article
- White blood cell classification via a discriminative region detection assisted feature aggregation network.Biomedical optics express · 2022Article
- Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models.ACS omega · 2022Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The year 2020 will certainly be remembered for the COVID-19 outbreak. First reported in Wuhan city of China back in December 2019, the number of people getting affected by this contagious virus has grown exponentially. Given the population density of India, the implementation of the mantra of the test, track, and isolate is not obtaining satisfactory results. A shortage of testing kits and an increasing number of fresh cases encouraged us to come up with a model that can aid radiologists in detecting COVID19 using chest Xray images. In the proposed framework the low level features from the Chest X-ray images are extracted using an ensemble of four pre-trained Deep Convolutional Neural Network (DCNN) architectures, namely VGGNet, GoogleNet, DenseNet, and NASNet and later on are fed to a fully connected layer for classification. The proposed multi model ensemble architecture is validated on two publicly available datasets and one private dataset. We have shown that our multi model ensemble architecture performs better than single classifier. On the publicly available dataset we have obtained an accuracy of 88.98% for three class classification and for binary class classification we report an accuracy of 98.58%. Validating the performance on private dataset we obtained an accuracy of 93.48%. The source code and the dataset are made available in the github linkhttps://github.com/sagardeepdeb/ensemble-model-for-COVID-detection.
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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.