Surrogate-Based Optimization of Interpretable Regular Expression Patterns for the Classification of Retinal Lesions in Retinal Fundus Images

The correct classification of retinal lesions in retinal fundus images is important for supporting the analysis of diabetic retinopathy and age-related macular degeneration. State-of-the-art methods for this task are often based on black-box deep learning architectures that, despite their high performance, pose significant interpretability challenges, incur high computational costs, and lack computational interpretability at the feature-decision level. In this paper, a method based on surrogate-optimized features extracted by regular expressions is proposed for the classification of two retinal lesion categories (Drusen and Cotton Wool Spots). The method uses a compact and computationally interpretable row-by-row and column-by-column regular expression feature extractor together with a two-phase surrogate search over its discrete hyperparameters. Across 100 independent stratified executions under the repeated patch-level benchmark, the proposed method achieved a mean MCC of 0.7829±0.0448, a mean accuracy of 0.9008±0.0217, and a mean F1 score of 0.8529±0.0294. The best execution reached an MCC of 0.8433, an accuracy of 0.9286, and a macro F1 score of 0.8966, which was the highest result among the evaluated baselines within that same benchmark. Additional source–image disjoint grouped analyses were carried out as leakage-aware robustness checks under stricter source–image separation and to examine validation overfitting. Together, these analyses support the usefulness of the compact run-based descriptor under the present experimental conditions, while indicating that the two-phase search should be interpreted as a practical hyperparameter selection heuristic rather than as a statistically superior search strategy.