Detection of Diseased Salmon Through Advanced Image Processing and Comparative Model Evaluation

Abstract

The rapid spread of diseases in aquaculture poses significant risks to fish health, operational efficiency, and food safety. Manual inspection of fish conditions is time-consuming, subjective, and ineffective for large-scale monitoring. This study presents an advanced image-based detection framework for identifying diseased salmon using modern deep learning architectures, including ResNet-18, EfficientNetV2, Vision Transformer (ViT), Swin Transformer, and ConvNeXt. The SalmonScan dataset was preprocessed through normalization, resizing, and data augmentation to enhance generalization. Each model was trained and evaluated using accuracy, precision, recall, and F1-score. Additionally, Grad-CAM visualization was applied to interpret model decisions and highlight infection-related regions on fish images. Experimental results demonstrate that transformer-based and next-generation CNN architectures significantly outperform conventional methods, with ConvNeXt and ViT achieving up to 99.59% accuracy. These findings indicate that modern deep learning models combined with explainable visual analysis provide a highly reliable and interpretable solution for automatic disease detection in salmon aquaculture.