Automated Fish Classification Using Convolutional Neural Networks

Abstract

Underwater habitats are home to about 34,800 fish species. Understanding the various fish species and their therapeutic properties is crucial. The majority of bodies of water have a large number of fish. They can be found in almost all aquatic settings, from high alpine streams (like char and gudgeon) to the abyssal and even hadal depths of the deepest oceans, despite the fact that no species have yet been discovered in the bottom 25% of the ocean (such as cusk-eels and snailfish). In the research, a model for categorizing the various fish species was devised. To ascertain the fish’s possible health advantages, different fish species are categorized. The transfer learning is used to pretrain models with our data which is then given to convolutional neural networks (CNNs), visual geometry group (VGG), residual neural network (RESNET), and densely connected neural network (DENSENET) with a moderate amount of change in the output layer. Additionally, this dataset is input to a CNN model with five sequential convolutional layers that uses the “Adam optimizer,” “ReLU,” and the “SoftMax” activation function. Furthermore, convolutional neural network is used with sixteen layers and softmax activation at the bottom to create the VGG model. The transfer learning is used for RESNET and DENSENET by changing the last layers. The 121 thick layers of the DENSENET that has used were interconnected forward and used Adam optimizer. The 50-layer RESNET, which employs skip connections with ReLU and softmax activation functions. By using classic CNN, we obtain an accuracy of 98.11%; with the help of VGG, and able to achieve an accuracy of 99%; with RESNET, a 99.56% accuracy; and finally, DENSENET, a 98.78% accuracy. Potential health advantages of fish.