A Deep Learning-Based System for Prawn Hepatopancreas Identification Based on Image Classification with Interpretability

Accurate identification of the hepatopancreas is essential for prawn quality assessment and automated seafood processing. This study presents an explainable deep learning framework for the binary classification of prawn images into “with hepatopancreas” and “no hepatopancreas” categories. A custom convolutional neural network (CNN) was developed using a dataset of 252 annotated images. To improve feature extraction from the limited dataset, an image preprocessing pipeline incorporating automatic contour-based cropping, contrast enhancement, and data augmentation was employed. The proposed model achieved a test accuracy of 97.37% and a calibrated full-dataset accuracy of 97.22%. Five-fold cross-validation yielded a mean accuracy of 96.83% ± 1.94%, indicating stable performance across different data partitions. Receiver operating characteristic analysis demonstrated satisfactory discriminative ability with AUC > 0.99. Gradient-weighted Class Activation Mapping (Grad-CAM) showed that the model primarily focused on biologically relevant hepatopancreas regions, improving the interpretability of the classification results. A graphical user interface was developed to enable rapid image analysis with visual feedback. Although further validation using larger and more diverse datasets is required, the proposed framework demonstrates the potential of explainable deep learning for automated prawn quality assessment and provides a practical foundation for intelligent seafood inspection applications.