Abstract: Accurate classification of fish species plays a crucial role in biodiversity conservation, fisheries management, and aquatic ecosystem monitoring. Traditionally, fish classification methods, rely on manual identification, which are time-consuming, error-prone, and often depend heavily on expert knowledge. To overcome these limitations, this study presents an Improved Classification Model for Fish Species Based on Deep Learning Techniques. The proposed model integrates Convolutional Neural Networks (CNNs) with advanced deep learning architectures such as VGG16, ResNet50 and optimized through transfer learning and fine-tuning strategies to enhance feature extraction and classification accuracy. Fish Image Dataset containing multiple species acquired from kaggle.com was utilized. The dataset was preprocessed using image augmentation techniques including rotation, scaling, flipping, and brightness adjustments. Images were normalized and resized to 224×224 pixels to fit the model input requirements. The study adopted a comparative experimental design where three CNN architectures were trained, validated, and tested on the same dataset. Each model was evaluated using accuracy, precision, recall and F1-score metrics to ensure robust performance assessment. Training was conducted using Python and TensorFlow/Keras frameworks. The experimental results revealed that the Improved ResNet50-based model achieved superior classification performance compared to baseline CNN and VGG16 models, attaining an overall accuracy of 96.2%, precision of 97.4%, recall of 96.2%, and an F1-score of 96.4% on the test set. The improved deep learning model provides a robust, scalable, and automated approach for fish species classification, offering substantial potential for applications in marine biology, aquaculture, and ecological research.
Keywords: Fish Image Recognition, Classification, CNN, VGG16, ResNet, Deep Learning.
Title: Improved Classification Model for Fish Species Based on Deep Learning Techniques
Author: Samuel Oluwatayo Ogunlana, Felix Ola Aranuwa
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 14, Issue 1, January 2026 - March 2026
Page No: 110-121
Research Publish Journals
Website: www.researchpublish.com
Published Date: 21-February-2026
