A BiLSTM model enhanced with multi-objective arithmetic optimization for COVID-19 diagnosis from CT images

In response to the relentless mutation of the coronavirus disease, current artificial intelligence algorithms for the automated diagnosis of COVID-19 via CT imaging exhibit suboptimal accuracy and efficiency. This manuscript proposes a multi-objective optimization algorithm (MOAOA) to enhance the Bi...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 10841 - 19
Hlavní autori:	Chen, Liang, Lin, Xin, Ma, Liangliang, Wang, Chao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 29.03.2025 Nature Publishing Group Nature Portfolio
Predmet:	631/114/1305 631/114/1564 631/114/2164 Accuracy Algorithms Artificial Intelligence Automatic diagnosis Automation BiLSTM Classification Comparative analysis Computed tomography Coronaviruses COVID-19 COVID-19 - diagnosis COVID-19 - diagnostic imaging Datasets Deep learning Diagnosis Disease Efficiency Human error Humanities and Social Sciences Humans Long short-term memory Medical imaging Medical research MOAOA Multi-objective optimization multidisciplinary Optimization algorithms Optimization techniques Patients R&D Research & development SARS-CoV-2 Science Science (multidisciplinary) Tomography Tomography, X-Ray Computed - methods COVID-19 MOAOA Multi-objective optimization Automatic diagnosis BiLSTM
ISSN:	2045-2322, 2045-2322
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