Analysis of Convolutional Neural Network-Based Detection of Fish Skin Diseases

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Abstract

This paper explores the application of Convolutional Neural Networks (CNNs) for automated detection and classification of fish skin diseases using image data. We highlight the critical need for timely and accurate disease identification in aquaculture to ensure sustainability and mitigate economic losses. Our study reviews various deep learning techniques, including transfer learning and ensemble methods, and examines the performance of prominent CNN architectures such as VGG- 16, MobileNet-V2, Inception-V3, and ResNet-50. We emphasize data preprocessing, augmentation strategies to overcome dataset limitations, and the importance of appropriate performance metrics like precision, accuracy, recall, and F1-score. Through our analysis, the CNN model achieved an overall accuracy of 92% in classifying fish skin diseases. This paper synthesizes current literature, identifies key challenges, and proposes future research in this rapidly evolving field.

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Keywords

Convolutional Neural Networks, Fish Diseases, Deep Learning, Image Classification, Aquaculture, Transfer Learning, Ensemble Methods.