# COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches

arXiv: https://arxiv.org/abs/2004.03747 · HTML: https://mlsanity.com/paper/2004.03747/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning](https://mlsanity.com/paper/2108.05067/) (2021)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans](https://mlsanity.com/paper/2101.05442/) (2021)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment](https://mlsanity.com/paper/2303.00279/) (2023)
- [Reliable COVID-19 Detection Using Chest X-ray Images](https://mlsanity.com/paper/2101.12254/) (2021)
- [U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction](https://mlsanity.com/paper/2108.00860/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data](https://mlsanity.com/paper/2108.08895/) (2021)
- [Detecting COVID-19 from Chest Computed Tomography Scans using AI-Driven Android Application](https://mlsanity.com/paper/2111.06254/) (2021)
- [Neural Networks for Infectious Diseases Detection: Prospects and Challenges](https://mlsanity.com/paper/2112.03571/) (2021)
- [A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT](https://mlsanity.com/paper/2207.07117/) (2022)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)

## Abstract (arXiv)

COVID-19 is currently one the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection is essential to identify, take better decisions and ensure treatment for the patients which will help save their lives. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19. The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images. A novel quantitative analysis strategy is also proposed in this paper to determine the percentage of infected regions in X-ray and CT images. The qualitative and quantitative results demonstrate promising results for COVID-19 detection and infected region localization.