Multi-level residual network VGGNet for fish species classification
The development of an image-based fish classification system using Convolutional Neural Network (CNN) has the advantages of no longer directly conducting features extraction and several features analysis. These steps has been involved by cascading convolution from initial to final block, where the i...
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Springer
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821001300 |
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| author | Eko Prasetyo Nanik Suciati Chastine Fatichah |
| author_facet | Eko Prasetyo Nanik Suciati Chastine Fatichah |
| author_sort | Eko Prasetyo |
| collection | DOAJ |
| description | The development of an image-based fish classification system using Convolutional Neural Network (CNN) has the advantages of no longer directly conducting features extraction and several features analysis. These steps has been involved by cascading convolution from initial to final block, where the initial, middle, and final blocks produce low-, middle-, and high-level features, respectively. Due to cascading convolution, CNN produces only high-level features. However, fish classification requires not only high-level features but also low-level features such as points, lines, and textures for representing edge spines, gill covers, fins, and skin textures in order to achieve higher performance; furthermore, CNN generally has not yet incorporated low-level features in the last block. In this paper, we proposed Multi-Level Residual (MLR) as a new residual network strategy by combining low-level features of the initial block with high-level features of the last block by applying Depthwise Separable Convolution. We also proposed MLR-VGGNet as a new CNN architecture inherited from VGGNet and strengthened it using Asymmetric Convolution, MLR, Batch Normalization, and Residual features. Our experimental results show that MLR-VGGNet achieved an accuracy of 99.69%, outperformed original VGGNet relative up to 10.33% and other CNN models relative up to 5.24% on Fish-gres and Fish4-Knowledge dataset. |
| format | Article |
| id | doaj-art-e4924bc3b4c94f67a1ac0e9a8c9fbedc |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-e4924bc3b4c94f67a1ac0e9a8c9fbedc2025-08-20T03:52:02ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-09-013485286529510.1016/j.jksuci.2021.05.015Multi-level residual network VGGNet for fish species classificationEko Prasetyo0Nanik Suciati1Chastine Fatichah2Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Jl. Raya ITS, Surabaya 60111, Indonesia; Department of Informatics, Faculty of Engineering, Universitas Bhayangkara Surabaya, Jl. Ahmad Yani 114, Surabaya 60231, IndonesiaDepartment of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Jl. Raya ITS, Surabaya 60111, Indonesia; Corresponding author.Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Jl. Raya ITS, Surabaya 60111, IndonesiaThe development of an image-based fish classification system using Convolutional Neural Network (CNN) has the advantages of no longer directly conducting features extraction and several features analysis. These steps has been involved by cascading convolution from initial to final block, where the initial, middle, and final blocks produce low-, middle-, and high-level features, respectively. Due to cascading convolution, CNN produces only high-level features. However, fish classification requires not only high-level features but also low-level features such as points, lines, and textures for representing edge spines, gill covers, fins, and skin textures in order to achieve higher performance; furthermore, CNN generally has not yet incorporated low-level features in the last block. In this paper, we proposed Multi-Level Residual (MLR) as a new residual network strategy by combining low-level features of the initial block with high-level features of the last block by applying Depthwise Separable Convolution. We also proposed MLR-VGGNet as a new CNN architecture inherited from VGGNet and strengthened it using Asymmetric Convolution, MLR, Batch Normalization, and Residual features. Our experimental results show that MLR-VGGNet achieved an accuracy of 99.69%, outperformed original VGGNet relative up to 10.33% and other CNN models relative up to 5.24% on Fish-gres and Fish4-Knowledge dataset.http://www.sciencedirect.com/science/article/pii/S1319157821001300Multi-level residualLow level featureConvolutional neural networkAsymmetric convolutionFish species classificationVGGNet |
| spellingShingle | Eko Prasetyo Nanik Suciati Chastine Fatichah Multi-level residual network VGGNet for fish species classification Journal of King Saud University: Computer and Information Sciences Multi-level residual Low level feature Convolutional neural network Asymmetric convolution Fish species classification VGGNet |
| title | Multi-level residual network VGGNet for fish species classification |
| title_full | Multi-level residual network VGGNet for fish species classification |
| title_fullStr | Multi-level residual network VGGNet for fish species classification |
| title_full_unstemmed | Multi-level residual network VGGNet for fish species classification |
| title_short | Multi-level residual network VGGNet for fish species classification |
| title_sort | multi level residual network vggnet for fish species classification |
| topic | Multi-level residual Low level feature Convolutional neural network Asymmetric convolution Fish species classification VGGNet |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821001300 |
| work_keys_str_mv | AT ekoprasetyo multilevelresidualnetworkvggnetforfishspeciesclassification AT naniksuciati multilevelresidualnetworkvggnetforfishspeciesclassification AT chastinefatichah multilevelresidualnetworkvggnetforfishspeciesclassification |