COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
By Md. Zahangir Alom, M. M. Rahman, Mst Shamima Nasrin, Tarek M. Taha, Vijayan K. Asari. 2020
170 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, methodology): “[12] use both X-ray images and CT images to do segmentation and detection.”
- U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “[386] established a multi-stage model to detect COVID-19 from X-ray and CT images.”