ArticleScientific reports2025
A BiLSTM model enhanced with multi-objective arithmetic optimization for COVID-19 diagnosis from CT images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Modelling of hybrid deep ensemble learning based skin lesion detection using FCNN denoising and inception-dilated ResNetV2.Scientific reports · 2026Article
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
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4 authors.
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Abstract
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 BiLSTM model for COVID-19 automated diagnosis. The proposed approach involves configuring several hyperparameters for the bidirectional long short-term memory (BiLSTM), optimized using the MOAOA intelligent optimization algorithm, and subsequently validated on publicly accessible medical datasets. Remarkably, our model achieves an impressive 95.32% accuracy and 95.09% specificity. Comparative analysis with state-of-the-art techniques demonstrates that the proposed model significantly enhances accuracy, efficiency, and other performance metrics, yielding superior results.
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