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Detecting COVID-19 on CT Images with Impulsive-Backpropagation Neural Networks
34th Chinese Control and Decision Conference, CCDC 2022 ; : 2797-2803, 2022.
Article in English | Scopus | ID: covidwho-2280826
ABSTRACT
This paper presents an impulsive-backpropagation neural network (IBNN) based learning algorithm for detecting Coronavirus Disease 2019 (COVID-19), by classifying chest computed tomography (CT) images. Inspired by the nerve impulses in brain networks, the IBNN algorithm consists of two parts a multi-layered network of impulsive neurons and a gradient decent backpropagation mechanism. The effectiveness of the IBNN algorithm is validated on clinical COVID-19 database, and a classification accuracy of 98.19% is achieved. It is further demonstrated by comparative studies that the IBNN may outperform some other learning algorithms through the integration of nerve impulses and backpropagation. Considering the intricate attributes of the chest CT scan images, the IBNN algorithm also exhibits a potential capacity of pattern recognition on complicated samples. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 34th Chinese Control and Decision Conference, CCDC 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 34th Chinese Control and Decision Conference, CCDC 2022 Year: 2022 Document Type: Article