Aiming at the problems of small sample size and low detection accuracy of diseased takifugu rubripes, a detection method of diseased takifugu rubripes based on ResNet50 and migration learning was proposed. First, ResNet50 was used to pre-train the model on the ImageNet dataset; then based on the pre-training results, the takifugu rubripes detection ResNet50 network was constructed, and the model weights, which were pre-trained and contained in 16 residual blocks, were transferred to the ResNet50 network for model weight initialization to reduce the training cost; in order to further improve the accuracy of detection, a deconvolution layer was added after the last convolution layer of the constructed ResNet50 network model to learn the details of the target; finally, the data set was constructed from the images of healthy and diseased takifugu rubripes, and the data was augmented using methods such as flipping, rotation, random cropping, chromaticity changes, and adding noise to increase the diversity of data samples and improve the robustness of the detection method. Experiments were conducted on the constructed dataset, the accuracy of the detection method for disinfected puffer fish based on ResNet50 and transfer learning can reach 99%. Compared with ResNet18, ResNet34, ResNet101 and ResNet152, the detection accuracy of the proposed method was improved by 10.7%, 6.6%, 6.2% and 5.6% respectively. Compared with the ResNet50 residual network without deconvolution, the detection accuracy of the proposed method was improved by 0.4%. The results showed that the method based on ResNet50 and transfer learning could effectively solve the problems of fewer samples and low accuracy of diseased puffer fish, and provide a reference for the study of diseased puffer fish detection