Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI
The accurate classification of fish species is vital for biodiversity monitoring and aquaculture management. In this study, a deep learning-based framework is proposed to classify bighead catfish (Clarias macrocephalus), North African catfish (Clarias gariepinus), and their F1 hybrids using cranial...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003971 |
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| author | Kriengsak Treeprapin Rakdee Bandatang Thitipong Panthum Worapong Singchat Jiraboon Prasanpan Kornsorn Srikulnath Suchin Trirongjitmoah |
| author_facet | Kriengsak Treeprapin Rakdee Bandatang Thitipong Panthum Worapong Singchat Jiraboon Prasanpan Kornsorn Srikulnath Suchin Trirongjitmoah |
| author_sort | Kriengsak Treeprapin |
| collection | DOAJ |
| description | The accurate classification of fish species is vital for biodiversity monitoring and aquaculture management. In this study, a deep learning-based framework is proposed to classify bighead catfish (Clarias macrocephalus), North African catfish (Clarias gariepinus), and their F1 hybrids using cranial morphological features. Eliminating the need for full-body analysis, this approach prioritizes head-based classification through YOLOv11 cranial segmentation, deep learning models (e.g., ResNet18), and dataset balancing techniques (e.g., augmentation) to enhance accuracy. The highest accuracy (99.517%) and F1-score (99.551%) were achieved using ResNet18, which demonstrated superior feature extraction capabilities and resilience to class imbalances. Classification reliability was ensured by genetic validation, while gradient-weighted class activation mapping (Grad-CAM) visualizations confirmed the significance of cranial features, particularly in the occipital region. The proposed framework demonstrates improved classification accuracy, offering a scalable and reliable alternative to conventional taxonomy. Future work could explore hybrid convolutional neural network (CNN) and vision transformers (ViT) models to optimize performance and efficiency. |
| format | Article |
| id | doaj-art-dfcfa8cc62a14fe99c739c0f12d7c9f6 |
| institution | Matheson Library |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-dfcfa8cc62a14fe99c739c0f12d7c9f62025-07-14T04:15:28ZengElsevierSmart Agricultural Technology2772-37552025-12-0112101165Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AIKriengsak Treeprapin0Rakdee Bandatang1Thitipong Panthum2Worapong Singchat3Jiraboon Prasanpan4Kornsorn Srikulnath5Suchin Trirongjitmoah6Department of Mathematics Statistics and Computer, Faculty of Science, Ubon Ratchathani University, 85 Sathonlamark, Warinchamrab, Ubon Ratchathani 34190, ThailandDepartment of Electrical and Electronics Engineering, Faculty of Engineering, Ubon Ratchathani University, 85 Sathonlamark, Warinchamrab, Ubon Ratchathani 34190, ThailandAnimal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Bangkok, Thailand; Biodiversity Center, Kasetsart University (BDCKU), Bangkok 10900, ThailandAnimal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Bangkok, Thailand; Biodiversity Center, Kasetsart University (BDCKU), Bangkok 10900, ThailandKalasin Fish Hatchery Farm, Betagro Public Company Limited, Buaban, Yangtalad District, Kalasin, ThailandAnimal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Bangkok, Thailand; Biodiversity Center, Kasetsart University (BDCKU), Bangkok 10900, Thailand; Corresponding authors.Department of Electrical and Electronics Engineering, Faculty of Engineering, Ubon Ratchathani University, 85 Sathonlamark, Warinchamrab, Ubon Ratchathani 34190, Thailand; Corresponding authors.The accurate classification of fish species is vital for biodiversity monitoring and aquaculture management. In this study, a deep learning-based framework is proposed to classify bighead catfish (Clarias macrocephalus), North African catfish (Clarias gariepinus), and their F1 hybrids using cranial morphological features. Eliminating the need for full-body analysis, this approach prioritizes head-based classification through YOLOv11 cranial segmentation, deep learning models (e.g., ResNet18), and dataset balancing techniques (e.g., augmentation) to enhance accuracy. The highest accuracy (99.517%) and F1-score (99.551%) were achieved using ResNet18, which demonstrated superior feature extraction capabilities and resilience to class imbalances. Classification reliability was ensured by genetic validation, while gradient-weighted class activation mapping (Grad-CAM) visualizations confirmed the significance of cranial features, particularly in the occipital region. The proposed framework demonstrates improved classification accuracy, offering a scalable and reliable alternative to conventional taxonomy. Future work could explore hybrid convolutional neural network (CNN) and vision transformers (ViT) models to optimize performance and efficiency.http://www.sciencedirect.com/science/article/pii/S2772375525003971Deep learningCatfish classificationCranial segmentationClass imbalanceExplainable AI |
| spellingShingle | Kriengsak Treeprapin Rakdee Bandatang Thitipong Panthum Worapong Singchat Jiraboon Prasanpan Kornsorn Srikulnath Suchin Trirongjitmoah Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI Smart Agricultural Technology Deep learning Catfish classification Cranial segmentation Class imbalance Explainable AI |
| title | Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI |
| title_full | Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI |
| title_fullStr | Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI |
| title_full_unstemmed | Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI |
| title_short | Enhancing clariid catfish species classification: A deep learning framework utilizing cranial morphology and explainable AI |
| title_sort | enhancing clariid catfish species classification a deep learning framework utilizing cranial morphology and explainable ai |
| topic | Deep learning Catfish classification Cranial segmentation Class imbalance Explainable AI |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003971 |
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