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| Main Authors: | , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Computers in biology and medicine
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40315719/ |
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| _version_ | 1868266209153122305 |
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| author | Hassan, Muhammad Salbitani, Giovanna Carfagna, Simona Khan, Javed Ali |
| author_facet | Hassan, Muhammad Salbitani, Giovanna Carfagna, Simona Khan, Javed Ali Hassan, Muhammad Salbitani, Giovanna Carfagna, Simona Khan, Javed Ali |
| collection | PubMed - marine biology |
| contents | Deep learning meets marine biology: Optimized fused features and LIME-driven insights for automated plankton classification. Hassan, Muhammad Salbitani, Giovanna Carfagna, Simona Khan, Javed Ali Deep Learning Plankton Marine Biology Animals Algorithms Plankton are microorganisms that play an important role in marine food webs as primary producers in the trophic web. Traditional plankton identification methods using manual microscopy and sampling are time-consuming, labor-intensive, and prone to errors. Deep learning has improved the automation of plankton identification, but it remains challenging to achieve high accuracy and efficiency in computation with limited labeled data. In this paper, we proposed an improved plankton classification model that is more accurate and interpretable. We train two models, InceptionResNetV2 (transfer learning) and DeepPlanktonNet (from scratch), on the WHOI dataset. We utilize feature fusion to supplement feature representation, merging the outputs of both models. Feature selection is achieved through the Whale Optimization Algorithm (WOA), eliminating redundancy and making it more computationally efficient. Additionally, we also employ Local Interpretable Model-agnostic Explanations (LIME) to make the model more interpretable and gain insights into how the model makes decisions. Additionally, feature selection using WOA reduces feature space and has less inference and computational cost. Our method achieves a classification accuracy of 98.79 %, which is better than previous state-of-the-art methods. For robustness testing, we train nine machine learning classifiers on the optimized features. By significantly improving classification accuracy and speed, our method enables large-scale ecological surveys, water quality monitoring, and biodiversity studies. These advances allow researchers to and environmental scientists to automate plankton classification more reliably, supporting marine conservation and resource management. |
| format | Artículo científico |
| id | pubmed_40315719 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Computers in biology and medicine |
| record_format | pubmed |
| spellingShingle | Deep learning meets marine biology: Optimized fused features and LIME-driven insights for automated plankton classification. Hassan, Muhammad Salbitani, Giovanna Carfagna, Simona Khan, Javed Ali Deep Learning Plankton Marine Biology Animals Algorithms Deep learning meets marine biology: Optimized fused features and LIME-driven insights for automated plankton classification. Hassan, Muhammad Salbitani, Giovanna Carfagna, Simona Khan, Javed Ali Deep Learning Plankton Marine Biology Animals Algorithms Plankton are microorganisms that play an important role in marine food webs as primary producers in the trophic web. Traditional plankton identification methods using manual microscopy and sampling are time-consuming, labor-intensive, and prone to errors. Deep learning has improved the automation of plankton identification, but it remains challenging to achieve high accuracy and efficiency in computation with limited labeled data. In this paper, we proposed an improved plankton classification model that is more accurate and interpretable. We train two models, InceptionResNetV2 (transfer learning) and DeepPlanktonNet (from scratch), on the WHOI dataset. We utilize feature fusion to supplement feature representation, merging the outputs of both models. Feature selection is achieved through the Whale Optimization Algorithm (WOA), eliminating redundancy and making it more computationally efficient. Additionally, we also employ Local Interpretable Model-agnostic Explanations (LIME) to make the model more interpretable and gain insights into how the model makes decisions. Additionally, feature selection using WOA reduces feature space and has less inference and computational cost. Our method achieves a classification accuracy of 98.79 %, which is better than previous state-of-the-art methods. For robustness testing, we train nine machine learning classifiers on the optimized features. By significantly improving classification accuracy and speed, our method enables large-scale ecological surveys, water quality monitoring, and biodiversity studies. These advances allow researchers to and environmental scientists to automate plankton classification more reliably, supporting marine conservation and resource management. |
| title | Deep learning meets marine biology: Optimized fused features and LIME-driven insights for automated plankton classification. |
| topic | Deep Learning Plankton Marine Biology Animals Algorithms |
| url | https://pubmed.ncbi.nlm.nih.gov/40315719/ |