Multi-level residual network VGGNet for fish species classification

Prasetyo, Eko and Suciati, Nanik and Fatichah, Chastine (2022) Multi-level residual network VGGNet for fish species classification. Journal of King Saud University – Computer and Information Sciences, 34 (8). pp. 5286-5295. ISSN 1319-1578

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Abstract

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, mid�dle, and final blocks produce low-, middle-, and high-level features, respectively. Due to cascading con�volution, 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 pro�posed 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.

Item Type:	Article
Uncontrolled Keywords:	Multi-level residual Low level feature Convolutional neural network Asymmetric convolution Fish species classification VGGNet
Subjects:	Technology
Divisions:	Faculty of Engineering
Depositing User:	Perpus Ubhara Surabaya
Date Deposited:	29 Apr 2024 06:11
Last Modified:	14 May 2024 04:41
URI:	http://eprints.ubhara.ac.id/id/eprint/2408

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