{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:53:54Z","timestamp":1774540434098,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Automated fish species classification is essential for marine biodiversity monitoring, fisheries management, and ecological research. However, challenges such as environmental variability, class imbalance, and computational demands hinder the development of robust classification models. This study investigates the effectiveness of convolutional neural network (CNN)-based models and hybrid approaches to address these challenges. Eight CNN architectures, including DenseNet121, MobileNetV2, and Xception, were compared alongside traditional classifiers like support vector machines (SVMs) and random forest. DenseNet121 achieved the highest accuracy (90.2%), leveraging its superior feature extraction and generalization capabilities, while MobileNetV2 balanced accuracy (83.57%) with computational efficiency, processing images in 0.07 s, making it ideal for real-time deployment. Advanced preprocessing techniques, such as data augmentation, turbidity simulation, and transfer learning, were employed to enhance dataset robustness and address class imbalance. Hybrid models combining CNNs with traditional classifiers achieved intermediate accuracy with improved interpretability. Optimization techniques, including pruning and quantization, reduced model size by 73.7%, enabling real-time deployment on resource-constrained devices. Grad-CAM visualizations further enhanced interpretability by identifying key image regions influencing predictions. This study highlights the potential of CNN-based models for scalable, interpretable fish species classification, offering actionable insights for sustainable fisheries management and biodiversity conservation.<\/jats:p>","DOI":"10.3390\/info16020154","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T09:36:22Z","timestamp":1739957782000},"page":"154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints"],"prefix":"10.3390","volume":"16","author":[{"given":"Amirhosein","family":"Mohammadisabet","sequence":"first","affiliation":[{"name":"Department of Computer Science, Solent University, Southampton SO14 0YN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-837X","authenticated-orcid":false,"given":"Raza","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Solent University, Southampton SO14 0YN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0661-1174","authenticated-orcid":false,"given":"Vishal","family":"Dattana","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Management Information System, Oman College of Management & Technology, P.O. Box 680, Barka 320, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-4095","authenticated-orcid":false,"given":"Salman","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nazeer Hussain University, ST-2, Near Karimabad, Karachi 75950, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0766-2402","authenticated-orcid":false,"given":"Saqib","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8QH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140412","DOI":"10.1109\/ACCESS.2024.3468438","article-title":"Automatic Estuarine Fish Species Classification System Based on Deep Learning Techniques","volume":"12","author":"Tejaswini","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. 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