Leveraging VGG16 for Fish Classification in a Large-Scale Dataset
DOI:
10.47709/brilliance.v3i2.3270Keywords:
Fish, Classification, CNN, VGG16, Deep LearningDimension Badge Record
Abstract
When the VGG16 model was applied to fish picture classification, the overall accuracy was a remarkable 99%, demonstrating strong performance over most of the dataset. Still, a thorough assessment of the model's efficacy necessitates a look beyond its general accuracy. A more detailed evaluation is possible thanks to class-specific metrics like precision, recall, and F1-score, which provide information on how well the model performs on particular classes. Although the high overall accuracy is encouraging, more research into these metrics and the possibility of class imbalances should be taken into account to guarantee consistent performance in the fish image classification challenge across all categories. A more comprehensive assessment of the model's effectiveness benefits from a contextual knowledge of the application domain and a careful examination of evaluation measures.
Abstract viewed = 121 times
References
Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M.A.; Al-Amidie, M.; Farhan, L. (2021). Review of Deep Learning: Concepts, Cnn Architectures, Challenges, Applications, Future Directions. Journal of Big Data Vol. 8 Number 53.
Belaid, O.N.; Loudini, M., (2020). Classication of Brain Tumor by Combination of Pre-Trained Vgg1 6 CNN. Journal of Information Technology Management2020, 12, 1 3-25.
Bhatt, D.; Patel, C.; Talsania, H.; Patel, J.; Vaghela, R.; Pandya, S.; Modi, K.; Ghayvat, H. (2021) CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics, 10, 2470.
Chen, M., Lai, T., Chen, Y., Chou, T., & Ning, F. (2023). A Robust Fish Species Classification Framework: FRCNN-VGG16-SPPNET. https://doi.org/10.21203/rs.3.rs-2825927/v1
Guo, P. and Gao Q., (2017). A multi-organ plant identification method using convolutional neural networks, in Proceedings of 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2017, pp. 371-376.
Hasan, Mohammed & Fatemi, Mohammed & Khan, Mohammad & Kaur, Manjit & Zaguia, Atef. (2021). Comparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks. Journal of Healthcare Engineering. 2021. 1-17. 10.1155/2021/5895156.
He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90
Hu, J., Li, D., Duan, Q., Han, Y., Chen, G., Si, X., (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput. Electron. Agric. 88, 133–140. https://doi.org/10.1016/j.compag.2012.07.008.
Khalifa, N. E. M., Taha, M. H. N. and Hassanien A. E., (2019). Aquarium Family Fish Species Identification System Using Deep Neural Networks, Advances in Intelligent Systems and Computing Proceedings of the International Conference on Advanced Intelligent.
Koushik, J. (2016). Understanding Convolutional Neural Networks. May 2016. Available online: http://arxiv.org/abs/1605.09081
Kratzert, F. and Mader, H., (2018) Fish species classification in underwater video monitoring using Convolutional Neural Networks, doi: 10.31223/osf.io/dxwtz.
Labao, A.B., Naval, P.C., (2019). Cascaded Deep Network Systems With Linked Ensemble Components For Underwater Fish Detection In The Wild. Ecol. Inf. 52, 103–121. https://doi.org/10.1016/j.ecoinf.2019.05.004.
Liu, S. and Deng, W., (2015). Very deep convolutional neural network based image classification using small training sample size, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, pp.730-734.
Mingwang, L., (2017). Fish Image Recognition and Separation Based on Convolutional Neural Network. Image and Multimedia Technology, Vol. 3, pp. 82-83.
Pardede, J.; Sitohang, B.; Akbar, S.; Khodra, M.L., (2021) Implementation of transfer learning using VGG1 6 on fruit ripeness detection. Int. J. Intell. Syst. Appl 2021, 13, 52-61.
Prakash, K.B.; Kannan, R.; Alexander, S.A.; Kanagachidambaresan, G.R. (2021). Advanced Deep Learning for Engineers and Scientists: A Practical Approach; Springer: Berlin/Heidelberg, Germany.
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, Volume 34, Issue 8, Part A, Pages 5286-5295, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2021.05.015.
Qin, H., Li, X., Liang, J., Peng, Y., Zhang, C., (2016). DeepFish: Accurate underwater live fish recognition with a deep architecture. Neurocomputing 187, 49–58. https://doi.org/10.1016/J.NEUCOM.2015.10.122.
Rathi, D., Jain, S. and Indu, S., Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning, (2017). Ninth International Conference on Advances in Pattern Recognition (ICAPR)Bangalore, pp.1-6. doi: 10.1109/ICAPR.2017.8593044.
Santos, A. A. D. and Gonçalves, W. N., (2019). Improving Pantanal Fish Species Recognition Through Taxonomic Ranks in Convolutional Neural Networks, Ecological Informatics, vol. 53, p. 100977, doi: 10.1016/j.ecoinf.2019.100977
Simonyan, K. and Zisserman A., (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Standford University, (2018). Introduction to Convolutional Neural Networks, https://web.stanford.edu/class/cs231a/lectures/intro_cnn.pdf.
Taye MM. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation. 11(3):52. https://doi.org/10.3390/computation11030052.
Younis, A.; Qiang, L.; Nyatega, C.O.; Adamu, M.J.; Kawuwa, H.B. (2022). Brain Tumor Analysis Using Deep Learning and Vgg-1 6 Ensembling Learning Approaches. Applied Sciences 2022, 12, 7282
Downloads
ARTICLE Published HISTORY
Accepted Date: 2023-11-29
Published Date: 2023-12-15
How to Cite
Issue
Section
License
Copyright (c) 2023 Karina Auliasari, Mohamed Wasef, Mariza Kertaningtyas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
info@itscience.org, web: 
