FeliniAI uses three-model pipeline to detect feline allergies with F1 score of 0.97

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FeliniAI is an AI-assisted diagnostic system designed to identify feline allergies by combining three complementary models rather than relying on a single one. A MobileNetV2 CNN analyzes images of a cat's skin and coat, achieving 93.4% visual accuracy with sub-second inference on CPU. An XGBoost classifier processes 33 clinical features derived from standardized ICADA criteria for feline atopic dermatitis, reaching a macro F1 score of 0.9675 across 8,000 cases using 5-fold cross-validation. A Llama 3.3 70B large language model then integrates outputs from both models to generate a readable recommendation, covering allergy type, confidence level, and suggested next steps. The project highlights that in sensitive diagnostic domains, an ensemble of specialized models can outperform a single large model by covering each other's blind spots.

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