# DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays

arXiv: https://arxiv.org/abs/2104.04006 · HTML: https://mlsanity.com/paper/2104.04006/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 73 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [Comparing Different Deep Learning Architectures for Classification of Chest Radiographs](https://mlsanity.com/paper/2002.08991/) (2020)
- [Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan](https://mlsanity.com/paper/2011.11750/) (2020)
- [COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios](https://mlsanity.com/paper/2004.05835/) (2020)
- [CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification](https://mlsanity.com/paper/2006.01441/) (2020)
- [Hypergraph Learning for Identification of COVID-19 with CT Imaging](https://mlsanity.com/paper/2005.04043/) (2020)
- [Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan](https://mlsanity.com/paper/2005.03405/) (2020)

## Built on by (most cited first, 5 of 5)

- [A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy](https://mlsanity.com/paper/2309.00911/) (2023)
- [AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images](https://mlsanity.com/paper/2410.15437/) (2024)
- [SF2Former: Amyotrophic Lateral Sclerosis Identification From Multi-center MRI Data Using Spatial and Frequency Fusion Transformer](https://mlsanity.com/paper/2302.10859/) (2023)
- [Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review](https://mlsanity.com/paper/2303.16754/) (2023)
- [Novel Pooling-based VGG-Lite for Pneumonia and Covid-19 Detection from Imbalanced Chest X-Ray Datasets](https://mlsanity.com/paper/2504.07468/) (2025)

## Abstract (arXiv)

The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment. Along with COVID-19, other etiologies of pneumonia and tuberculosis constitute additional challenges to the medical system. In this regard, the objective of this work is to develop a new deep transfer learning pipeline to diagnose patients with COVID-19, pneumonia, and tuberculosis, based on chest x-ray images. We observed in some instances DenseNet and Resnet have orthogonal performances. In our proposed model, we have created an extra layer with convolutional neural network blocks to combine these two models to establish superior performance over either model. The same strategy can be useful in other applications where two competing networks with complementary performance are observed. We have tested the performance of our proposed network on two-class (pneumonia vs healthy), three-class (including COVID-19), and four-class (including tuberculosis) classification problems. The proposed network has been able to successfully classify these lung diseases in all four datasets and has provided significant improvement over the benchmark networks of DenseNet, ResNet, and Inception-V3. These novel findings can deliver a state-of-the-art pre-screening fast-track decision network to detect COVID-19 and other lung pathologies.