As the proportion of aquaculture in the fisheries industry increases, it becomes increasingly important to address the problems that arise. To prevent the rapid spread of diseases in fish farms, this paper proposes a deep learning-based image classification model. The performance of VGG16, ResNet50, Xception, MViTv2, DaViT, and CoAtNet was compared for the classification of fish diseases such as bleeding, defects, and necrosis. Precision, recall, F1 score, and accuracy were used as performance metrics. Our experimental findings reveal that the CoAtNet model exhibits the highest recall and accuracy rates, both reaching 0.6410.