Abstract:
Fish is a popular food all around the world Because of its
excellent nutritional content. Furthermore, fish is low in fat and high
in protein. The nutritional value of various fish varies. Fish are essential
experimental animals in a variety of fields of biological and medical
research. A solid foundation for understanding the more adaptable behavior
of higher vertebrates has been established by research on fish.
This article focused on the classification of two types of fish: local and
coastal fish. This will aid in identifying fish, and this article will also
provide knowledge of numerous fish species identifications, allowing researchers
to study the nutritional value of fish. The local and coastal
fish categories contain twelve different fish species: Catla, Cyprinus Carpio,
Grass Carp, Mori, Rohu, Silver, Black Sea Sprat, Gilt Head Bream,
Red Sea Bream, Horse Mackerel, Sea Bass, and Trout. Moreover, there
are 13,176 fish shots in the dataset used in this article. In addition, to
identify the species, fish are labeled with unique integer values. A deep
learning based approach has been applied to classify the fish species in
this article. A Convolutional Neural Network (CNN) technique has been
used in this research work as CNN provides high-quality performance in
the field of image segmentation. Hence, the proposed model achieves a
satisfactory result of 98.33%.
