Investigation of cardinality classification for bacterial colony counting using explainable artificial intelligence

Automatic bacterial colony counting is a highly sought-after technology in modernbiological laboratories because it eliminates manual counting effort. Previouswork has observed that MicrobiaNet, currently the best-performing cardinalityclassification model for colony counting, has difficulty distinguishing coloniesof three or more individuals. However, it is unclear if this is due to propertiesof the data together with inherent characteristics of the MicrobiaNet model. By analysing MicrobiaNet with explainable artificial intelligence (XAI), we demonstrate that XAI can provide insights into how data properties constrain cardinality classification performance in colony counting. Our results show that high visual similarity across classes is the key issue hindering further performance improvement, revising prior assertions about MicrobiaNet. These findings suggest future work should focus on models that explicitly incorporate visual similarity or explore density estimation approaches, with broader implications for neural network classifiers trained on imbalanced datasets.