DeepFishNet: A Hybrid Deep Learning Model for Real-Time Fish Disease Classification

Classification of fish diseases stands as a vital underwater aquaculture task which enables prompt detection and control of infections. Convolutional neural networks (CNNs) enable deep learning to act as a strong automated disease detection mechanism through which complex image patterns get extracted. The research develops a YOLOv9 based deep learning model enhancement which performs classification on 11 fish diseases. The improved version of this model includes extra convolutional layers with connected layers supporting a classification layer through Softmax activation for better fish disease detection performance. Evaluation and training tasks were performed on FishLens dataset. Our proposed model reaches an mAP@50 evaluation value of 81% while surpassing YOLOv8 detection by 2%. Real time fish disease classification effectiveness emerges from our method which provides an automated monitoring solution for aquaculture systems.