Inflammation is a common occurrence and a significant contributor to poor health in today’s modern way of life. Finding a drug that is safe and effective in controlling inflammation is a challenge. This is why numerous animal models have been created to assess drugs with anti-inflammatory properties. However, it is crucial to carefully choose suitable animal models during the initial stages of drug development. Traditional methods for evaluating inflammation in animal models rely on time-consuming manual data analysis. In this paper, the authors propose a machine-learning approach that combines segmentation and classification models to evaluate the inflammation level of mouse paws. The system leverages the Segment Anything Model to segment the paw and then applies a lightness classifier to determine the degree of infection. Due to the limitation of the dataset, although direct training or fine-tuning an instance segmentation model can yield results, the performance is suboptimal, limiting the model’s accuracy and reliability. We use a prompt generator component which creates a bounding box to guide the segment process of Segment Anything. Our system emphasizes flexibility since it is built for various models integrated into the pipeline. Our proposed approach demonstrates high segmentation accuracy, achieving a Mean Dice Score of 0.97997, along with high classification accuracy in our benchmark, even with minimal data. This emphasizes its practical utility in biomedical analysis.