This paper presents a new application of a hybrid dropout technique for Ordinal Classification (OC), based on a novel regularisation method. Unlike standard dropout, which ignores class ordering, this hybrid dropout integrates ordinal information by adjusting neurons dropout probabilities based on their correlation with target labels. We evaluate its effectiveness using a ResNet18 architecture over three new OC datasets and compare it with the standard dropout approach and with an architecture with no dropout. Results show that the hybrid dropout consistently achieves the best performance across multiple well-known metrics (1-off, QWK, MAE, AMAE, and RPS), while also reducing prediction variability. Statistical analysis using the Wilcoxon signed-rank test confirms its robustness, obtaining 21 significant wins out of 30 comparisons, with no losses. These results highlight the importance of designing regularisation strategies that consider the problems ordinal structure, demonstrating that hybrid dropout effectively enhances generalisation and predictive accuracy.