A method of detecting fish diseases with CEH-YOLOv8 based on dual-channel and hierarchical synergism was developed to solve the problems of the irregular shapes， unclear textures， and scattered disease spots making it difficult to localize the true lesion areas in the detection of fish diseases. A dual-channel feature extraction network was introduced to enhance the ability of model to extract irregular lesion areas with unclear textures. Then， an efficient channel spatial attention （ECSA） mechanism was proposed to improve the capability of model to recognize distributed targets. A hierarchical and balanced feature pyramid network （HBFPN） for was presented to reinforce the improved backbone network and perform hierarchical feature fusion on the information extracted from the backbone network at different levels to enhance the ability of model to express feature. The results showed that the CEH-YOLOv8 network had an accuracy rate of 83.2%， a recall rate of 72.5%， and a mean average precision （mAP） of 76.2% in detecting fish diseases， respectively. Compared with the state-of-the-art （SOAT）YOLOv10 method and the original model， it increased the accuracy rate， recall rate， and mAP by 6.9， 11.6， and 11.9 percent points， and 4.3， 6.9， and 6 percent points， respectively. The inference time for a single frame was 9.1 ms. It is indicated that the improved YOLOv8 network can accurately screen fish with diseases and be used for early detection of fishery diseases to reduce economic losses.