ArticleScientific reports2024
Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed.
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- Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection.Scientific reports · 2026Article
- A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection.Scientific reports · 2026Article
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- Evaluation of therapeutic efficacy in hepatocellular carcinoma ablation based on CT radiomics and deep learning multimodal models.Frontiers in oncology · 2026Article
- Intelligent information management enables quality-by-design in pharmaceutical production.Scientific reports · 2025Article
- A novel framework using particle swarm optimization and long short-term memory networks for stock market forcasting.Scientific reports · 2025Article
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- SHAP enhanced transformer GWO boosting model for transparent and robust anomaly detection in IIoT environments.Scientific reports · 2025Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).
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