  Radio galaxies exhibit a rich diversity of characteristics and emit radio
emissions through a variety of radiation mechanisms, making their
classification into distinct types based on morphology a complex challenge. To
address this challenge effectively, we introduce an innovative approach for
radio galaxy classification using COSFIRE filters. These filters possess the
ability to adapt to both the shape and orientation of prototype patterns within
images. The COSFIRE approach is explainable, learning-free, rotation-tolerant,
efficient, and does not require a huge training set. To assess the efficacy of
our method, we conducted experiments on a benchmark radio galaxy data set
comprising of 1180 training samples and 404 test samples. Notably, our approach
achieved an average accuracy rate of 93.36\%. This achievement outperforms
contemporary deep learning models, and it is the best result ever achieved on
this data set. Additionally, COSFIRE filters offer better computational
performance, $\sim$20$\times$ fewer operations than the DenseNet-based
competing method (when comparing at the same accuracy). Our findings underscore
the effectiveness of the COSFIRE filter-based approach in addressing the
complexities associated with radio galaxy classification. This research
contributes to advancing the field by offering a robust solution that
transcends the orientation challenges intrinsic to radio galaxy observations.
Our method is versatile in that it is applicable to various image
classification approaches.
