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Research Scholar, Department of Computer Science, VELS Institute of Science, Technology & Advanced Studies (VISTAS) , Chennai, Tamil Nadu , India
Professor, Head, Department of Computer Science, VELS Institute of Science, Technology & Advanced Studies (VISTAS) , Chennai, Tamil Nadu , India
The marine ecoculture is an evolving realm that necessitates thorough scrutiny of the diverse species it comprises, along with the explicit identification of the species classes that form, to be crucial for aquaculture and the ecological conservation of fish diversity. The stratification through image classification is a well-studied area of research using various conventional algorithmic methods. However, the need to progressively identify deep features to stratify the multiple species of fish unambiguously remains the pivotal study of this investigation. Deep learning methodologies utilize various pre-trained networks that enable the identification of fish species through a systematic, layered approach of non-linear activation functions, which delineate feature patterns and thereby achieve higher classification accuracy. The study proposes a new method for stratifying fish species by applying transfer learning to pre-trained deep learning networks, namely AlexNet, InceptionV3, and Resnet-18, along with the application of ShuffleRes, a hybrid deep network classifier. To address the specified research question, the work employs transfer learning, which enables the exploitation of knowledge from large image datasets by fine-tuning pre-trained models. This approach enhances classification performance in fish species, despite the limited availability of annotated data. Furthermore, the proposed ShuffleRes architecture combines the advantages of residual connections and shuffle layers, promoting improved feature representation, discriminative capacity, and surpassing classification accuracy compared to individual pre-trained networks. The simulations are implemented in MATLAB, and the results for the study are successfully procured.
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