Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/2252
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
Md Zahangir Alom.
M M Shaifur Rahman.
Mst Shamima Nasrin.
Tarek M. Taha.
Vijayan K. Asari.
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://arxiv.org/pdf/2004.03747v2.pdf
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.
arxiv.org
2020
Artículo
https://arxiv.org/pdf/2004.03747v2.pdf
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

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