Deep Feature Optimization for Enhanced Fish Freshness Assessment

• URL: http://arxiv.org/abs/2510.24814v1
• Date: Tue, 28 Oct 2025 09:02:10 GMT
• Title: Deep Feature Optimization for Enhanced Fish Freshness Assessment
• Authors: Phi-Hung Hoang, Nam-Thuan Trinh, Van-Manh Tran, Thi-Thu-Hong Phan,
• Abstract summary: Assessing fish freshness is vital for ensuring food safety and minimizing economic losses in the seafood industry.<n>Recent advances in deep learning have automated visual freshness prediction, but challenges related to accuracy and feature transparency persist.<n>This study introduces a unified three-stage framework that refines and leverages deep visual representations for reliable fish freshness assessment.
• Score: 0.05599792629509228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Assessing fish freshness is vital for ensuring food safety and minimizing economic losses in the seafood industry. However, traditional sensory evaluation remains subjective, time-consuming, and inconsistent. Although recent advances in deep learning have automated visual freshness prediction, challenges related to accuracy and feature transparency persist. This study introduces a unified three-stage framework that refines and leverages deep visual representations for reliable fish freshness assessment. First, five state-of-the-art vision architectures - ResNet-50, DenseNet-121, EfficientNet-B0, ConvNeXt-Base, and Swin-Tiny - are fine-tuned to establish a strong baseline. Next, multi-level deep features extracted from these backbones are used to train seven classical machine learning classifiers, integrating deep and traditional decision mechanisms. Finally, feature selection methods based on Light Gradient Boosting Machine (LGBM), Random Forest, and Lasso identify a compact and informative subset of features. Experiments on the Freshness of the Fish Eyes (FFE) dataset demonstrate that the best configuration combining Swin-Tiny features, an Extra Trees classifier, and LGBM-based feature selection achieves an accuracy of 85.99%, outperforming recent studies on the same dataset by 8.69-22.78%. These findings confirm the effectiveness and generalizability of the proposed framework for visual quality evaluation tasks.

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