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CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image
Rabia Musheer Aziz1, Rajul Mahto2, Aryan Das2
1Mathematics division, School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India.
Chemistry & Biodiversity
|July 2, 2023
Summary
This study introduces an advanced deep learning model for accurate fish image classification, achieving 100% accuracy. This method surpasses traditional techniques and other leading models for identifying fish species and aiding in disease detection.
Area of Science:
- Ichthyology and Computer Vision
- Application of Artificial Intelligence in Biological Sciences
Background:
- Accurate fish species identification is vital for monitoring seafood diseases and decay, as symptoms vary significantly between species.
- Traditional methods for fish classification are often slow and cumbersome, necessitating more efficient automated approaches.
- Understanding fish population distribution and geographic patterns is crucial for advancing fisheries science.
Purpose of the Study:
- To identify the optimal strategy for fish image classification using advanced computer vision, data mining, and optimization algorithms.
- To develop and validate a novel deep learning model for high-accuracy fish species identification.
- To compare the proposed model's performance against established deep learning architectures.
Main Methods:
- Utilized the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA) combined with data mining techniques for feature extraction.
- Developed a Proposed Deep Learning Model for image classification.
- Benchmarked the Proposed Deep Learning Model against Convolutional Neural Networks (CNN), VGG-19, ResNet150V2, DenseNet, Inception V3, and Xception.
Main Results:
- The Proposed Deep Learning Model achieved a perfect accuracy rate of 100% in fish image classification.
- The proposed method significantly outperformed other state-of-the-art models, which achieved accuracies ranging from 98.48% to 99.63%.
- Empirical validation using artificial neural networks confirmed the superiority of the Proposed Deep Learning Model.
Conclusions:
- The developed Proposed Deep Learning Model represents a highly effective and accurate solution for fish image classification.
- This AI-driven approach offers a significant improvement over traditional methods, enabling faster and more precise identification.
- The model's high accuracy has substantial implications for fisheries management, disease monitoring, and biological research.
Keywords:
chaotic oppositional based whale optimization algorithm (COWOA)convolutional neural networks (CNN)deep learning modelfeature extractionfish image classificationMore Related Videos
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