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

• URL: http://arxiv.org/abs/2004.03747v3
• Date: Sat, 18 Apr 2020 19:01:10 GMT
• Title: COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
• Authors: Md Zahangir Alom, M M Shaifur Rahman, Mst Shamima Nasrin, Tarek M. Taha, and Vijayan K. Asari
• Abstract summary: We propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. X-ray and CT scan images are considered to evaluate the proposed technique. The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images.
• Score: 5.578413517654704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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