To address the issue of misclassification in the image recognition system, I would start by analyzing the model's performance metrics to identify specific categories where the misclassification is occurring. This would involve reviewing confusion matrices and other relevant evaluation metrics. Next, I would examine the dataset to ensure it is well-balanced and representative of the categories. If necessary, I would augment the dataset with additional samples to improve diversity.

Once the dataset is verified, I would look into the model's architecture and hyperparameters to see if adjustments are needed. This could involve experimenting with different model architectures or tuning hyperparameters to enhance performance. I would also consider implementing techniques like transfer learning, which can leverage pre-trained models to improve accuracy with less data.

To ensure minimal downtime, I would set up a parallel testing environment where these changes can be validated without affecting the production system. Once the improvements are confirmed, I would deploy the updated model using a phased rollout or A/B testing to monitor its performance in real-world conditions, ensuring a smooth transition with minimal disruption.