tetano
Editor, Senior Moderator
Sci Rep
. 2025 Mar 29;15(1):10841.
doi: 10.1038/s41598-025-94654-2. A BiLSTM model enhanced with multi-objective arithmetic optimization for COVID-19 diagnosis from CT images
Liang Chen[SUP] 1 [/SUP], Xin Lin[SUP] 2 [/SUP], Liangliang Ma[SUP] 2 [/SUP], Chao Wang[SUP] 3 [/SUP]
Affiliations
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.
Keywords: Automatic diagnosis; BiLSTM; COVID-19; MOAOA; Multi-objective optimization.
. 2025 Mar 29;15(1):10841.
doi: 10.1038/s41598-025-94654-2. A BiLSTM model enhanced with multi-objective arithmetic optimization for COVID-19 diagnosis from CT images
Liang Chen[SUP] 1 [/SUP], Xin Lin[SUP] 2 [/SUP], Liangliang Ma[SUP] 2 [/SUP], Chao Wang[SUP] 3 [/SUP]
Affiliations
- PMID: 40155431
- PMCID: PMC11953258
- DOI: 10.1038/s41598-025-94654-2
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.
Keywords: Automatic diagnosis; BiLSTM; COVID-19; MOAOA; Multi-objective optimization.