XGBoost Implementation for Marine Fish Image Classification Using VGG19-Based Transfer Learning

Abstract

Abstract. Accurate identification of fish species is important for trade supervision, marine biology, and fisheries management. This study develops a classification system for nine marine fish species by combining VGG19-based feature extraction with XGBoost. Using 9,000 balanced RGB images from the Kaggle Large-Scale Fish Dataset, the workflow includes EDA, preprocessing, an 80:20 data split, feature extraction, and model training. EDA showed that Red Mullet and Striped Red Mullet share similar visual characteristics, while the Shrimp class appears more distinct. The model achieved 97% accuracy and was deployed through a Flask web application that processes, extracts features, and classifies images. These findings demonstrate that integrating VGG19 with XGBoost offers an effective and efficient approach for marine fish image classification. Purpose: The primary objective here is to build a hybrid classification model for marine fish that is both efficient and robust, merging deep feature extraction from the VGG19 architecture with the XGBoost classifier. Specifically, this research seeks to improve classification accuracy while reducing computational overhead, thereby providing a reliable automated identification system that can support fisheries management, ecological monitoring, and marine biological research. Methods: This research employed a dataset of 9,000 images representing nine marine fish species. The preprocessing stage included resizing each image to 224×224 pixels and applying normalization. Deep feature representations were obtained using a pre-trained VGG19 model, utilizing only its convolutional layers to extract semantic features. These features were then classified with the XGBoost algorithm, chosen for its efficiency, resilience, and strong performance on structured data. The model was trained using an 80:20 split for training and testing, and its performance was assessed through accuracy, precision, recall, F1-score, and a confusion matrix. Result/Findings: The combination of VGG19 feature extraction with XGBoost produced strong results, achieving an accuracy of about 97%. This performance was further supported by consistently high precision, recall, and F1-scores across all nine species. Novelty/Value: A hybrid architecture integrating VGG19-based transfer learning with the XGBoost classifier is presented with an approach rarely explored in this domain. This combination capitalizes on deep semantic feature extraction from VGG19 alongside efficient structured classification via XGBoost, producing a model that delivers high accuracy with significantly reduced computational costs. Additionally, the hybrid approach proposed in this study remains competitive with fully end-to-end deep learning models yet operates with fewer parameters and reduced training demands. These results illustrate that well-designed hybrid pipelines can match the performance of conventional deep learning architecture while providing clear benefits in terms of training efficiency, interpretability, and scalability.