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Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.

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第一作者： Rukundo,Prince

第一单位： Beijing institute of technology, China. Electronic address: rukuprince@gmail.com.

作者： Rukundo,Prince ^[1] ; Zhendong,Niu ^[2] ; Zahid Younas,Khan ^[3] ; James,Chambua ^[4] ; Abdallah,Yousif ^[5] ; Niyishaka,Patrick ^[6] ; Batamuliza,Jennifer ^[7]

作者单位： Beijing institute of technology, China. Electronic address: rukuprince@gmail.com. ^[1] Beijing institute of technology, China. Electronic address: zniu@bit.edu.cn. ^[2] University of Azad Jammu and Kashmir, Pakistan. Electronic address: zyounask@gmail.com. ^[3] University of Dar es Salaam, Tanzania. Electronic address: jchambua@gmail.com. ^[4] Sudan Technological University, Sudan. Electronic address: abdosif33@hotmail.com. ^[5] University of Hyderabad, India. Electronic address: niyishakapatrick@gmail.com. ^[6] University of Rwanda, Rwanda. Electronic address: bajelo2000@yahoo.co.uk. ^[7]

关键词 COVID-19 Contextual adaptation multiscale gabor network Dynamic co-occurrence grey level matrix Feature importance Interpretability Sequential neural network

医学主题词人类(Humans);算法(Algorithms);放射摄影术, 胸部(Radiography, Thoracic);放射摄影影像解释, 计算机辅助(Radiographic Image Interpretation, Computer-Assisted);肺(Lung)

DOI 10.1016/j.compbiomed.2025.109659

PMID 39847942

发布时间 2025-05-02

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准确一般字义偏差语意偏离译文作用小提交