Deep Learning based Disease Prediction Using External and Internal Images of Paralichthys Olivaceus

Early detection and rapid response to fish disease are essential because fish farms are vulnerable to disease. However, since conventional fish disease diagnosis relies on expert knowledge, it takes a lot of time and money, raising the mortality risk. Recently, to prevent this, research on fish disease prediction using deep learning has been actively conducted. This study proposes a method to diagnose fish diseases by predicting disease symptoms using a deep-learning classification model and an object-detection model on internal and external images of Paralichthys olivaceus. First, images of internal organs are extracted from internal images of Paralichthys olivaceus using a deep-learning segmentation model, and internal symptoms are predicted by training the deep-learning classification model. Next, a deep-learning object-detection model is trained on external images of the Paralichthys olivaceus to detect external symptoms. The internal and external symptoms are then merged and used to predict diseases. We compared the performance of symptom weight-based, data statistics-based, and symptom set learning-based methods in disease prediction. The proposed internal-symptom classification by organ was superior to the symptom object detection, and predicting disease by learning the symptom set using deep learning achieved the highest performance with Top-1 accuracy of 0.5818, and Top-3 accuracy of 0.8364.