Integration of contrast-adaptive colour correction and convolutional neural network for cryptocaryon fish disease detection

Integrating Contrast Colour Correction (CACC) and Convolutional Neural Networks (CNN) can help fish breeders in earlier classification and identification of Cryptocaryon fish disease (protozoan white spot disease). Disease identification accuracy is enhanced through such method by adaptive colour changes and CNN feature extracting ability, thereby boosting underwater image clarity. Unlike traditional rule-based systems that relies on expert knowledge despite being error-prone, existing methods focus on visual quality without classifying impact influence. Early disease identification is hampered in terms of efficiency due to machine learning methods reliant on abundant human expertise other than efficient feature extracting. An artificial intelligence (AI)-oriented computer model is introduced for existing research in overcoming limitations and eliminating subjectivity. The model employs a proprietary method of diagnosing fish disease through underwater images examination that yields objective outcome. Several CNN structures such as GoogleNet, ResNet-101, AlexNet, ResNet-50 as well as VGG-16 are tested on its performance. The current study shows integration of CACC with CNN through a set of 15000 images boosting up model performance in Cryptocaryon fish disease detection. The introduced novel method significantly enhances performance with 99.53% accuracy, 99.08% precision along with 100.00% recall. This efficient, accurate approach can significantly reduce the workload of experts and fish farmers while promoting sustainable aquaculture and healthier aquatic ecosystems. • Contrast-Adaptive Colour Correction (CACC) and Convolutional Neural Network (CNN) enhance fish images for accurate Cryptocaryon fish disease detection. • 6500 image datasets from National Fish Health Research aids AI training and testing. • One of the CNN architectures, ResNet50 outperforms others with 99.52 % accuracy. • 99 % accuracy across key metrics ensures early disease detection. • AI-driven approach supports sustainable aquaculture and reduces losses.