Diabetic retinopathy (DR) is a major cause of vision impairment, with early detection playing a crucial role in preventing irreversible blindness. While deep learning-based automated DR grading has improved diagnostic efficiency, class imbalance in public datasets hinders reliable performance evaluation, particularly for underrepresented DR stages. Current state-of-the-art classifiers achieve high overall accuracy but suffer from poor balanced accuracy, limiting their real-world applicability. Inspired by recent advancements in diffusion models, we propose to mitigate class imbalance by generating synthetic fundus images. Unlike prior methods prioritizing visual quality, we introduce a semantic quality metric based on classifier-predicted likelihood to selectively filter synthetic samples that enhance classification performance. Furthermore, we incorporate explicit class constraint during diffusion model finetuning to generate more semantically relevant data. Experimental results demonstrate a significant improvement in balanced classification accuracy from 66.84% to 74.20%, highlighting the effectiveness of our approach in improving DR diagnosis. Our code is available at: https://github.com/AlanZhang1995/ECC_DM_for_DR.git .