Antimicrobial Resistance Prediction in Salmonella enterica Using Frequency Chaos Game Representation and ResNet-18

Antimicrobial resistance (AMR) prediction from bacterial genomes remains a major challenge for clinical microbiology and surveillance. We developed a deep learning model based on Frequency Chaos Game Representation (FCGR) and a ResNet-18 architecture to classify resistance phenotypes directly from whole-genome assemblies. Using homology-aware clustering to prevent genomic data leakage, we trained and evaluated models for Salmonella enterica (seven antibiotics) and Staphylococcus aureus (five antibiotics). The Salmonella model achieved high predictive accuracy, particularly for cephalosporins, while performance was lower for tetracycline and ampicillin. The Staphylococcus aureus model demonstrated that the pipeline generalizes to Gram-positive bacteria, with strong results for methicillin (Balanced Accuracy = 0.85). Benchmarking against the gene-based tool ResFinder showed that the FCGR-based model did not match the performance of ResFinder on most antibiotics, but achieved competitive results for cephalosporins. This study demonstrates the feasibility of applying FCGR-based deep learning to AMR prediction across bacterial species, though substantial improvements would be needed before clinical application.