CNN and Transformer-Based Deep Learning Models for Coral Health Classification

Coral reefs are among the most important marine ecosystems, providing habitat for nearly 32% of marine species and supporting the livelihoods and food security of more than 500 million people worldwide. In addition to sustaining marine biodiversity, coral reefs protect coastlines and contribute to economic and cultural activities in many regions. Despite their significance, coral reefs are rapidly declining due to climate change, ocean warming, and, in particular, coral bleaching. The most severe global coral bleaching event to date began in 2023 and is still ongoing, affecting approximately 84% of the world’s coral reefs, highlighting the urgent need for reliable methods to detect bleaching at an early stage. In response to this challenge, this paper proposes deep learning-based models for coral health classification using underwater images. The approach compares the performance of three convolutional neural network architectures, ResNet50, EfficientNetB3, and ConvNeXt-Tiny, with a transformer-based model, Swin Transformer-Tiny, for classifying corals as healthy or bleached. Transfer learning is applied to all models, and their performance is evaluated using a publicly available dataset containing 923 labeled coral images. The results show that all models achieve effective classification performance, with ConvNeXt-Tiny and Swin Transformer-Tiny attaining the highest accuracy of 86.33%, outperforming ResNet50 and EfficientNetB3. These findings provide insight into the advantages of newer CNN and transformer-based architectures for learning complex visual patterns in underwater coral images. The results further demonstrate their suitability for practical coral reef monitoring systems, supporting reliable and early detection of bleaching in real-world conservation and reef management applications