Classification network of COVID-19 based on multi-modality fusion network

Zhan, Z.; Qin, Y.; Du, P.; Ma, H.; Zhang, X.; Tang, Y.; Lv, S.; Li, Q.; Li, S.; Bai, P.; Zhou, G.

Zhan, Z.; Qin, Y.; Du, P.; Ma, H.; Zhang, X.; Tang, Y.; Lv, S.; Li, Q.; Li, S.; Bai, P.; Zhou, G..

2021 Ethics and Explainability for Responsible Data Science Conference, EE-RDS 2021 ; 2021.

Article in English | Scopus | ID: covidwho-1741176

ABSTRACT

ABSTRACT

Since 2019, COVID-19 has been a major problem for the world's population. COVID-19 is known for its fast transmission and strong infection. Therefore, how to reduce the burden of medical system is becoming a hot topic in current research. Previous researchers have used deep learning techniques to effectively classify COVID-19. Although the results are remarkable, the input method (X-ray images) is simple. Therefore, a new multi-modality fusion network is proposed in this paper. In this network, the spatial and structural feature information in the image were highlighted by means of thermal map. Experiments show the effectiveness of the proposed network. © 2021 IEEE.

Keywords

COVID-19; Heatmap; Multi-modality fusion; 'current; Classification networks; Fast transmissions; Heatmaps; Hot topics; Learning techniques; Medical systems; World population; Deep learning

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 Ethics and Explainability for Responsible Data Science Conference, EE-RDS 2021 Year: 2021 Document Type: Article

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