A benchmark analysis of saliency-based explainable deep learning methods for the morphological classification of radio galaxies

Abstract

This work proposes a saliency-based attribution framework to evaluate and compare 10 state-of-the-art explainability methods for deep learning models in astronomy, focusing on the classification of radio galaxy images. While previous work has primarily emphasized classification accuracy, we prioritize model interpretability. Qualitative assessments reveal that Score-CAM, Grad-CAM, and Grad-CAM++ consistently produce meaningful attribution maps, highlighting the brightest regions of FRI and FRII galaxies in alignment with known astrophysical features. In contrast, other methods often emphasize irrelevant or noisy areas, reducing their effectiveness.