Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection
IntroductionLung nodule detection is a crucial task for diagnosis and lung cancer prevention. However, it can be extremely difficult to identify tiny nodules in medical images since pulmonary nodules vary greatly in shape, size, and location. Further, the implemented methods have certain limitations...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1511716/full |
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| author | R. Hari Prasada Rao Agam Das Goswami |
| author_facet | R. Hari Prasada Rao Agam Das Goswami |
| author_sort | R. Hari Prasada Rao |
| collection | DOAJ |
| description | IntroductionLung nodule detection is a crucial task for diagnosis and lung cancer prevention. However, it can be extremely difficult to identify tiny nodules in medical images since pulmonary nodules vary greatly in shape, size, and location. Further, the implemented methods have certain limitations including scalability, robustness, data availability, and false detection rate.MethodsTo overcome the limitations in the existing techniques, this research proposes the Cnidaria Herd Optimization (CHO) algorithm-enabled Bi-directional Long Short-Term Memory (CHSTM) model for effective lung nodule detection. Furthermore, statistical and texture descriptors extract the significant features that aid in improving the detection accuracy. In addition, the FC2R segmentation model combines the optimized fuzzy C-means clustering algorithm and the Resnet −101 deep learning approach that effectively improves the performance of the model. Specifically, the CHO algorithm is modelled using the combination of the induced movement strategy of krill with the time control mechanism of the cnidaria to find the optimal solution and improve the CHSTM model’s performance.ResultsAccording to the experimental findings of a performance comparison between other established methods, the FC2R + CHSTM model achieves 98.09% sensitivity, 97.71% accuracy, and 97.03% specificity for TP 80 utilizing the LUNA-16 dataset. Utilizing the LIDC/IDRI dataset, the proposed approach attained a high accuracy of 97.59%, sensitivity of 96.77%, and specificity of 98.41% with k-fold validation outperforming the other existing techniques.ConclusionThe proposed FC2R + CHSTM model effectively detects lung nodules with minimum loss and better accuracy. |
| format | Article |
| id | doaj-art-af55c048e6284e7caf9b98b10d996552 |
| institution | DOAJ |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-af55c048e6284e7caf9b98b10d9965522025-08-20T02:56:36ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-03-011610.3389/fphys.2025.15117161511716Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detectionR. Hari Prasada RaoAgam Das GoswamiIntroductionLung nodule detection is a crucial task for diagnosis and lung cancer prevention. However, it can be extremely difficult to identify tiny nodules in medical images since pulmonary nodules vary greatly in shape, size, and location. Further, the implemented methods have certain limitations including scalability, robustness, data availability, and false detection rate.MethodsTo overcome the limitations in the existing techniques, this research proposes the Cnidaria Herd Optimization (CHO) algorithm-enabled Bi-directional Long Short-Term Memory (CHSTM) model for effective lung nodule detection. Furthermore, statistical and texture descriptors extract the significant features that aid in improving the detection accuracy. In addition, the FC2R segmentation model combines the optimized fuzzy C-means clustering algorithm and the Resnet −101 deep learning approach that effectively improves the performance of the model. Specifically, the CHO algorithm is modelled using the combination of the induced movement strategy of krill with the time control mechanism of the cnidaria to find the optimal solution and improve the CHSTM model’s performance.ResultsAccording to the experimental findings of a performance comparison between other established methods, the FC2R + CHSTM model achieves 98.09% sensitivity, 97.71% accuracy, and 97.03% specificity for TP 80 utilizing the LUNA-16 dataset. Utilizing the LIDC/IDRI dataset, the proposed approach attained a high accuracy of 97.59%, sensitivity of 96.77%, and specificity of 98.41% with k-fold validation outperforming the other existing techniques.ConclusionThe proposed FC2R + CHSTM model effectively detects lung nodules with minimum loss and better accuracy.https://www.frontiersin.org/articles/10.3389/fphys.2025.1511716/fulllung nodule detectionfuzzy c-means clusteringResnet −101cnidaria herd optimizationlobe segmentationdeep learning |
| spellingShingle | R. Hari Prasada Rao Agam Das Goswami Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection Frontiers in Physiology lung nodule detection fuzzy c-means clustering Resnet −101 cnidaria herd optimization lobe segmentation deep learning |
| title | Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection |
| title_full | Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection |
| title_fullStr | Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection |
| title_full_unstemmed | Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection |
| title_short | Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection |
| title_sort | cnidaria herd optimized fuzzy c means clustering enabled deep learning model for lung nodule detection |
| topic | lung nodule detection fuzzy c-means clustering Resnet −101 cnidaria herd optimization lobe segmentation deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1511716/full |
| work_keys_str_mv | AT rhariprasadarao cnidariaherdoptimizedfuzzycmeansclusteringenableddeeplearningmodelforlungnoduledetection AT agamdasgoswami cnidariaherdoptimizedfuzzycmeansclusteringenableddeeplearningmodelforlungnoduledetection |