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Deep Learning Models Performance on Marine Fish Species Classification
Corresponding Author(s) : Ezmahamrul Afreen Awalludin
Jurnal Ilmiah Perikanan dan Kelautan, 2025: IN PRESS ISSUE (JUST ACCEPTED MANUSCRIPT, 2025)
Abstract
Graphical Abstract
Highlight Research
- The ResNet50 presented the highest accuracy for classifying 20 marine fish species in the study.
- The performance comparison demonstrated that ResNet50 outperformed both AlexNet and GoogLeNet.
- Transfer learning enabled effective feature extraction from limited datasets.
- Deep learning models offer potential for automating the classification of marine fish
Abstract
Identifying marine fish species accurately can be difficult due to their subtle anatomical and colour pattern similarities, which often result in misclassification during ecological assessments and fisheries operations. Manual identification methods are time-consuming and prone to errors especially in high throughput environments such as fish markets. In this study, transfer learning is used to evaluate three deep learning models ResNet-50, AlexNet and GoogLeNet on a total of 20,325 images from twenty marine fish species acquired from Kuantan (Pahang) and Mengabang Telipot (Kuala Nerus), Malaysia. All images were morphologically classified as complete fish, head, body and tail. The dataset was subjected to preprocessing procedures which encompassed image resizing, pixel normalization and data augmentation techniques that consists of random rotation (±15°), horizontal flipping, adjustments to brightness and contrast (±20%) and cropping. Subsequently, the dataset was partitioned into 80% training set (16,260 images), 10% validation set (2,032 images) and 10% testing set (2,033 images). The classification patterns were analysed using confusion matrices and standard metrics such as accuracy, precision and recall. ResNet-50 outperformed other models achieving ideal results with 100% accuracy, precision and recall in every category. With 99.5% and 99.4% accuracy, GoogleNet and AlexNet came in second and third, respectively. This study shows that deep learning models especially ResNet-50 achieved an accurate and efficient way to classify fish species automatically. With multi-view images, data augmentation and transfer learning, the model performs well even in difficult visual conditions. These results support its use in real-time fisheries monitoring, biodiversity studies, and environmental impact assessments
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- Ahmad, U., Ali, M. J., Khan, F. A., Khan, A. A., Ur Rehman, A., Shahid, M. M. A., Haq, M. A., Khan, I., Alzamil, Z. S., & Alhussen, A. (2023). Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture. Computer Systems Science and Engineering, 45(2): 2125–2140.
- Ahmed, F., Basak, B., Chakraborty, S., Karmokar, T., Reza, A. W., Imam, O. T., & Arefin, M. S. (2023). Developing a Classification CNN Model to Classify Different Types of Fish. Lecture Notes in Networks and Systems. Springer.
- Alinsug M.V., Delos Reyes I.V.P., Maquiling A.C.C., Bercades A.J., Deocaris C.C. (2024). Skipjack tuna (Katsuwonus pelamis) otolith developmental stage classification using deep learning. Philippine Journal of Fisheries. 31(2):291-298.
- Allken V.; Handegard N.O.; Rosen S.; Schreyeck T.; Mahiout T.; Malde K. (2019). Fish species identification using a convolutional neural network trained on synthetic data. Journal of Marine Science. 76(1);342:349.
- Ben Tamou A.; Benzinou A.; Nasreddine K. (2022). Live fish species classification in underwater images by using convolutional neural networks based on incremental learning with knowledge distillation loss. Joural of Machine Learning and Knowledge Extraction, 4(3):753-767.
- Deka, J., Laskar, S., &Baklial, B. (2023). Automated freshwater fish species classification using deep cnn. Journal of The Institution of Engineers (India): Series B, 104(3): 603–621.
- Dong G., Wang N., Xu T., Liang J., Qiao R., Yin D., Lin S.(2023). Deep learning-enabled morphometric analysis for toxicity screening using zebrafish larvae. Journal of Environmental Science and Technology, 57(46): 18127-18138.
- French G., Mackiewicz M., Fisher M., Holah H., Kilburn R., Campbell N., Needle C. (2020). Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards. Journal of Marine Science, 77(4):1340-1353.
- Han Y., Chang Q., Ding S., Gao M., Zhang B., Li S. (2022). Research on multiple jellyfish classification and detection based on deep learning. Journal of Multimedia Tools and Applications, 81(14):19429 - 19444.
- Hasan, N., Ibrahim, S., & Aqilah Azlan, A. (2022). Fish diseases detection using convolutional neural network (CNN). International Journal of Nonlinear Analysis and Applications, 13(1), 1977–1984.
- Hilal A.M., Hashim A.H.A., Mohamed H.G., Nour M.K., Asiri M.M., Al-Sharafi A.M., Othman M., Motwakel A. (2023). Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning. Journal of Computers, Materials and Continua. 74(1):607-621.
- Hou Y., Canul-Ku M., Cui X., Hasimoto-Beltran R., Zhu M. (2021). Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach. Journal of Micropalaeontology, 40(2):163-173.
- Iqbal, M. A., Wang, Z., Ali, Z. A., & Riaz, S. (2021). Automatic fish species classification using deep convolutional neural networks. Wireless Personal Communications, 116(2):1043–1053.
- Iqtait M., Alqaryouti M.H., Sadeq A.E., AburommanA.,Baniata M., Mustafa Z., Chan H.Y. (2024). Enhanced fish species detection and classification using a novel deep learning approach. International Journal of Advanced Computer Science and Applications. 15(10):1063-1067.
- Ismail, H., Faisal, A., Ayob, M., Muhamed, A. @, Muslim, S. M., Fakhratul, M., &Zulkifli, R. (2021). Convolutional neural network architectures performance evaluation for fish species classification. Journal of Sustainability Science and Management, 16(5):124–139.
- Kaya, V., Akgül, İ., &ZencirTanir, Ö. (2023). IsVoNet8: A proposed deep learning model for classification of some fish species. Journal of Agricultural Sciences, 29(1):298–307.
- Knausgård, K.M., Wiklund, A., Sørdalen, T.K., Halvorsen, K.T., Kleiven, A.R., Jiao, L., & Goodwin, M. (2022). Temperate fish detection and classification: a deep learning-based approach. Applied Intelligence, 52 (6): 6988 - 7001.
- Li B.; Liu Y.; Duan Q.(2024). T-KD: two-tier knowledge distillation for a lightweight underwater fish species classification model. Aquaculture International Journal. 3(2): 3107-3128.
- Lan, X., Bai, J., Li, M., & Li, J. (2020). Fish Image Classification Using Deep Convolutional Neural Network. ACM International Conference Proceeding Series, 18–22.
- Okafor E., Schomaker L., Wiering M.A. (2018). An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals. Journal of Information and Telecommunication, 2(4):465-491.
- Peddina K., MandavaA.K.(2025). The intelligent object detection framework for detecting fish from underwater image. International Journal of Communication Networks and Distributed Systems. 31(1):63:88.
- Prasetyo, E., Suciati, N., &Fatichah, C. (2022). Multi-level residual network VGGNet for fish species classification. Journal of King Saud University - Computer and Information Sciences, 34(8): 5286–5295.
- Rauf, H. T., Lali, M. I. U., Zahoor, S., Shah, S. Z. H., Rehman, A. U., & Bukhari, S. A. C. (2019). Visual features based automated identification of fish species using deep convolutional neural networks. Computers and Electronics in Agriculture, 167(12):1-10.
- Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review,” Neural Computation, 29(9):2352–2449,
- Saleh, A., Sheaves, M., & Rahimi Azghadi, M. (2022). Computer vision and deep learning for fish classification in underwater habitats: A survey. Fish and Fisheries, 23(4):977–999.
- Song, J., Gao, S., Zhu, Y., & Ma, C. (2019). A survey of remote sensing image classification based on CNNs. Big Earth Data, 3(3), 232–254.
- Sun, M., Yang, X., & Xie, Y. (2020). Deep learning in aquaculture: A review. Journal of Computer, 31(1):294-319.
- Veluswami J.R.S., PanneerselvamN.(2022). Multi-species fish identification using hybrid deep CNN with refined squeeze and excitation architecture. Journal of Aquatic Sciences and Engineering. 37(4):220-228.
- Wang F., Zheng J., Zeng J., Zhong X., Li Z. (2023). S2F-YOLO: An optimized object detection technique for improving fish classification. Journal of Internet Technology, 24(6):1211-1220.
- Wang, P., Fan, E., & Wang, P. (2021). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141(1): 61–67.
- Xu M., Yang H., Liu G., Tang Y., Li W.(2022). In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches. Journal of Applied Toxicology. 42(11):1766-1776.
- Yang, C., Zhou, P., Wang, C. S., Fu, G. Y., Xu, X. W., Niu, Z., Zhu, L., Yuan, Y., Shen, H. Bin, & Pan, X. (2024). FishAI: Automated hierarchical marine fish image classification with vision transformer. Engineering Reports, 6(12).
- Yasin E.T., Ozkan I.A., Koklu M. (2023). Detection of fish freshness using artificial intelligence methods. Journal of European Food Research and Technology, 249(8):1979-1990.
- Zhai, J., Han, L., Xiao, Y., Yan, M., Wang, Y., & Wang, X. (2023). Few-shot fine-grained fish species classification via sandwich attention CovaMNet. Frontiers in Marine Science, 10, 1149186.
- Zhang, S., Liu, W., Zhu, Y., Han, W., Huang, Y., & Li, J. (2022). Research on fish identification in tropical waters under unconstrained environment based on transfer learning. Earth Science Informatics, 15(2):1155–1166.
- Zheng, S., Wang, R., Zheng, S., Wang, L., & Jiang, H. (2024). Adaptive density guided network with CNN and Transformer for underwater fish counting. Journal of King Saud University - Computer and Information Sciences, 36(6), 102088.
- Zhou, W., Wang, H., & Wan, Z. (2022). Ore Image Classification Based on Improved CNN. Computers and Electrical Engineering, 99(4):1-9 107819.
- Zhou, Z., Yang, X., Ji, H., & Zhu, Z. (2023). Improving the classification accuracy of fishes and invertebrates using residual convolutional neural networks. ICES Journal of Marine Science, 80(5): 1256–1266.
References
Ahmad, U., Ali, M. J., Khan, F. A., Khan, A. A., Ur Rehman, A., Shahid, M. M. A., Haq, M. A., Khan, I., Alzamil, Z. S., & Alhussen, A. (2023). Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture. Computer Systems Science and Engineering, 45(2): 2125–2140.
Ahmed, F., Basak, B., Chakraborty, S., Karmokar, T., Reza, A. W., Imam, O. T., & Arefin, M. S. (2023). Developing a Classification CNN Model to Classify Different Types of Fish. Lecture Notes in Networks and Systems. Springer.
Alinsug M.V., Delos Reyes I.V.P., Maquiling A.C.C., Bercades A.J., Deocaris C.C. (2024). Skipjack tuna (Katsuwonus pelamis) otolith developmental stage classification using deep learning. Philippine Journal of Fisheries. 31(2):291-298.
Allken V.; Handegard N.O.; Rosen S.; Schreyeck T.; Mahiout T.; Malde K. (2019). Fish species identification using a convolutional neural network trained on synthetic data. Journal of Marine Science. 76(1);342:349.
Ben Tamou A.; Benzinou A.; Nasreddine K. (2022). Live fish species classification in underwater images by using convolutional neural networks based on incremental learning with knowledge distillation loss. Joural of Machine Learning and Knowledge Extraction, 4(3):753-767.
Deka, J., Laskar, S., &Baklial, B. (2023). Automated freshwater fish species classification using deep cnn. Journal of The Institution of Engineers (India): Series B, 104(3): 603–621.
Dong G., Wang N., Xu T., Liang J., Qiao R., Yin D., Lin S.(2023). Deep learning-enabled morphometric analysis for toxicity screening using zebrafish larvae. Journal of Environmental Science and Technology, 57(46): 18127-18138.
French G., Mackiewicz M., Fisher M., Holah H., Kilburn R., Campbell N., Needle C. (2020). Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards. Journal of Marine Science, 77(4):1340-1353.
Han Y., Chang Q., Ding S., Gao M., Zhang B., Li S. (2022). Research on multiple jellyfish classification and detection based on deep learning. Journal of Multimedia Tools and Applications, 81(14):19429 - 19444.
Hasan, N., Ibrahim, S., & Aqilah Azlan, A. (2022). Fish diseases detection using convolutional neural network (CNN). International Journal of Nonlinear Analysis and Applications, 13(1), 1977–1984.
Hilal A.M., Hashim A.H.A., Mohamed H.G., Nour M.K., Asiri M.M., Al-Sharafi A.M., Othman M., Motwakel A. (2023). Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning. Journal of Computers, Materials and Continua. 74(1):607-621.
Hou Y., Canul-Ku M., Cui X., Hasimoto-Beltran R., Zhu M. (2021). Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach. Journal of Micropalaeontology, 40(2):163-173.
Iqbal, M. A., Wang, Z., Ali, Z. A., & Riaz, S. (2021). Automatic fish species classification using deep convolutional neural networks. Wireless Personal Communications, 116(2):1043–1053.
Iqtait M., Alqaryouti M.H., Sadeq A.E., AburommanA.,Baniata M., Mustafa Z., Chan H.Y. (2024). Enhanced fish species detection and classification using a novel deep learning approach. International Journal of Advanced Computer Science and Applications. 15(10):1063-1067.
Ismail, H., Faisal, A., Ayob, M., Muhamed, A. @, Muslim, S. M., Fakhratul, M., &Zulkifli, R. (2021). Convolutional neural network architectures performance evaluation for fish species classification. Journal of Sustainability Science and Management, 16(5):124–139.
Kaya, V., Akgül, İ., &ZencirTanir, Ö. (2023). IsVoNet8: A proposed deep learning model for classification of some fish species. Journal of Agricultural Sciences, 29(1):298–307.
Knausgård, K.M., Wiklund, A., Sørdalen, T.K., Halvorsen, K.T., Kleiven, A.R., Jiao, L., & Goodwin, M. (2022). Temperate fish detection and classification: a deep learning-based approach. Applied Intelligence, 52 (6): 6988 - 7001.
Li B.; Liu Y.; Duan Q.(2024). T-KD: two-tier knowledge distillation for a lightweight underwater fish species classification model. Aquaculture International Journal. 3(2): 3107-3128.
Lan, X., Bai, J., Li, M., & Li, J. (2020). Fish Image Classification Using Deep Convolutional Neural Network. ACM International Conference Proceeding Series, 18–22.
Okafor E., Schomaker L., Wiering M.A. (2018). An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals. Journal of Information and Telecommunication, 2(4):465-491.
Peddina K., MandavaA.K.(2025). The intelligent object detection framework for detecting fish from underwater image. International Journal of Communication Networks and Distributed Systems. 31(1):63:88.
Prasetyo, E., Suciati, N., &Fatichah, C. (2022). Multi-level residual network VGGNet for fish species classification. Journal of King Saud University - Computer and Information Sciences, 34(8): 5286–5295.
Rauf, H. T., Lali, M. I. U., Zahoor, S., Shah, S. Z. H., Rehman, A. U., & Bukhari, S. A. C. (2019). Visual features based automated identification of fish species using deep convolutional neural networks. Computers and Electronics in Agriculture, 167(12):1-10.
Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review,” Neural Computation, 29(9):2352–2449,
Saleh, A., Sheaves, M., & Rahimi Azghadi, M. (2022). Computer vision and deep learning for fish classification in underwater habitats: A survey. Fish and Fisheries, 23(4):977–999.
Song, J., Gao, S., Zhu, Y., & Ma, C. (2019). A survey of remote sensing image classification based on CNNs. Big Earth Data, 3(3), 232–254.
Sun, M., Yang, X., & Xie, Y. (2020). Deep learning in aquaculture: A review. Journal of Computer, 31(1):294-319.
Veluswami J.R.S., PanneerselvamN.(2022). Multi-species fish identification using hybrid deep CNN with refined squeeze and excitation architecture. Journal of Aquatic Sciences and Engineering. 37(4):220-228.
Wang F., Zheng J., Zeng J., Zhong X., Li Z. (2023). S2F-YOLO: An optimized object detection technique for improving fish classification. Journal of Internet Technology, 24(6):1211-1220.
Wang, P., Fan, E., & Wang, P. (2021). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141(1): 61–67.
Xu M., Yang H., Liu G., Tang Y., Li W.(2022). In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches. Journal of Applied Toxicology. 42(11):1766-1776.
Yang, C., Zhou, P., Wang, C. S., Fu, G. Y., Xu, X. W., Niu, Z., Zhu, L., Yuan, Y., Shen, H. Bin, & Pan, X. (2024). FishAI: Automated hierarchical marine fish image classification with vision transformer. Engineering Reports, 6(12).
Yasin E.T., Ozkan I.A., Koklu M. (2023). Detection of fish freshness using artificial intelligence methods. Journal of European Food Research and Technology, 249(8):1979-1990.
Zhai, J., Han, L., Xiao, Y., Yan, M., Wang, Y., & Wang, X. (2023). Few-shot fine-grained fish species classification via sandwich attention CovaMNet. Frontiers in Marine Science, 10, 1149186.
Zhang, S., Liu, W., Zhu, Y., Han, W., Huang, Y., & Li, J. (2022). Research on fish identification in tropical waters under unconstrained environment based on transfer learning. Earth Science Informatics, 15(2):1155–1166.
Zheng, S., Wang, R., Zheng, S., Wang, L., & Jiang, H. (2024). Adaptive density guided network with CNN and Transformer for underwater fish counting. Journal of King Saud University - Computer and Information Sciences, 36(6), 102088.
Zhou, W., Wang, H., & Wan, Z. (2022). Ore Image Classification Based on Improved CNN. Computers and Electrical Engineering, 99(4):1-9 107819.
Zhou, Z., Yang, X., Ji, H., & Zhu, Z. (2023). Improving the classification accuracy of fishes and invertebrates using residual convolutional neural networks. ICES Journal of Marine Science, 80(5): 1256–1266.