Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.
Early Lung Cancer (LC) detection is essential for reducing the global mortality rate. The limitations of traditional diagnostic techniques cause challenges in identifying LC using medical imaging data. In this study, we aim to develop a robust LC detection model. Positron Emission Tomography / Compu...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0313386 |
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| author | Abdul Rahaman Wahab Sait Eid AlBalawi Ramprasad Nagaraj |
| author_facet | Abdul Rahaman Wahab Sait Eid AlBalawi Ramprasad Nagaraj |
| author_sort | Abdul Rahaman Wahab Sait |
| collection | DOAJ |
| description | Early Lung Cancer (LC) detection is essential for reducing the global mortality rate. The limitations of traditional diagnostic techniques cause challenges in identifying LC using medical imaging data. In this study, we aim to develop a robust LC detection model. Positron Emission Tomography / Computed Tomography (PET / CT) images are utilized to comprehend the metabolic and anatomical data, leading to optimal LC diagnosis. In order to extract multiple LC features, we enhance MobileNet V3 and LeViT models. The weighted sum feature fusion technique is used to generate unique LC features. The extracted features are classified using spline functions, including linear, cubic, and B-spline of Kolmogorov-Arnold Networks (KANs). We ensemble the outcomes using the soft-voting approach. The model is generalized using the Lung-PET-CT-DX dataset. Five-fold cross-validation is used to evaluate the model. The proposed LC detection model achieves an impressive accuracy of 99.0% with a minimal loss of 0.07. In addition, limited resources are required to classify PET / CT images. The high performance underscores the potential of the proposed LC detection model in providing valuable and optimal results. The study findings can significantly improve clinical practice by presenting sophisticated and interpretable outcomes. The proposed model can be enhanced by integrating advanced feature fusion techniques. |
| format | Article |
| id | doaj-art-472ee7dd9e424352865281f1656d81ec |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-472ee7dd9e424352865281f1656d81ec2025-01-08T05:32:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031338610.1371/journal.pone.0313386Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.Abdul Rahaman Wahab SaitEid AlBalawiRamprasad NagarajEarly Lung Cancer (LC) detection is essential for reducing the global mortality rate. The limitations of traditional diagnostic techniques cause challenges in identifying LC using medical imaging data. In this study, we aim to develop a robust LC detection model. Positron Emission Tomography / Computed Tomography (PET / CT) images are utilized to comprehend the metabolic and anatomical data, leading to optimal LC diagnosis. In order to extract multiple LC features, we enhance MobileNet V3 and LeViT models. The weighted sum feature fusion technique is used to generate unique LC features. The extracted features are classified using spline functions, including linear, cubic, and B-spline of Kolmogorov-Arnold Networks (KANs). We ensemble the outcomes using the soft-voting approach. The model is generalized using the Lung-PET-CT-DX dataset. Five-fold cross-validation is used to evaluate the model. The proposed LC detection model achieves an impressive accuracy of 99.0% with a minimal loss of 0.07. In addition, limited resources are required to classify PET / CT images. The high performance underscores the potential of the proposed LC detection model in providing valuable and optimal results. The study findings can significantly improve clinical practice by presenting sophisticated and interpretable outcomes. The proposed model can be enhanced by integrating advanced feature fusion techniques.https://doi.org/10.1371/journal.pone.0313386 |
| spellingShingle | Abdul Rahaman Wahab Sait Eid AlBalawi Ramprasad Nagaraj Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. PLoS ONE |
| title | Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. |
| title_full | Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. |
| title_fullStr | Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. |
| title_full_unstemmed | Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. |
| title_short | Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification. |
| title_sort | ensemble learning driven kolmogorov arnold networks based lung cancer classification |
| url | https://doi.org/10.1371/journal.pone.0313386 |
| work_keys_str_mv | AT abdulrahamanwahabsait ensemblelearningdrivenkolmogorovarnoldnetworksbasedlungcancerclassification AT eidalbalawi ensemblelearningdrivenkolmogorovarnoldnetworksbasedlungcancerclassification AT ramprasadnagaraj ensemblelearningdrivenkolmogorovarnoldnetworksbasedlungcancerclassification |