A quantitative diagnosis method for
freshwater fish diseases based on OpenCV weak supervision and ResNet50-ADCA dual-
branch model is proposed. The method consists of the following steps: (1)
Image acquisition and preprocessing: Based on the publicly available
freshwater fish disease dataset, a balanced Salmon++ dataset is constructed by combining samples collected from
aquaculture sites; (2) Weakly supervised
lesion segmentation and labeling: (2.1) HSV
color space targeted segmentation; (2.2) Iterative optimization mechanism; (2.3)
Disease grading standard; (3) ResNet50-ADCA dual-
branch model construction: (3.1)
Backbone network and transfer learning; (3.2) SEA channel attention embedding; (3.3) Dual-
branch multi-task output layer design; (3.4) Classification branch; (3.5) Grading branch; (3.6)
Loss function design; (4) Model training and optimization. This invention balances labeling efficiency,
diagnostic accuracy, and quantitative grading capability, and can effectively solve the problems of high labeling cost, poor adaptability to complex environments, lack of quantitative
evaluation function, and imbalance between accuracy and speed in the existing technology.