FishNet Freshwater Fish Disease Detection using Deep Learning Techniques

Fish diseases pose significant challenges to aquaculture, causing economic losses and environmental issues. Early and accurate detection is important for effective management and prevention of diseases. This study proposes using three deep convolutional neural network architectures: Inception, VGG16, and a new CNN model called Fishnet. The efficacy of the models is assessed in two scenarios: using transfer learning and without it. The new FishNet model outperforms the pre-trained VGG16 network, demonstrating a 2% improvement in accuracy for disease detection in freshwater fishes.