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Diabetic retinopathy (DR) poses a major challenge in vision care, often leading to partial or complete limited vision in adults. Early and accurate detection of DR is essential for timely intervention and effective patient management. One critical aspect of DR diagnosis is identifying hard exudates in retinal images. Using deep tech or computer-aided methods, ophthalmologists can gain detailed insights into retinal health, facilitating precise diagnosis and treatment planning. However, the availability of medical image data in open datasets is often limited, making deep analysis challenging. To address this issue, we introduce a U-Net deep learning model, specifically modified and designed to segment hard exudates effectively, even with a limited dataset. In our study, the model is trained and tested on 89 original images from the DiaRetDB1 dataset, supplemented with augmented image to improve robustness. Despite the small dataset, our modified U-Net achieves high sensitivity, specificity, precision, and accuracy, culminating in an impressive F1 score of 0.9754. These results demonstrate the potential of our approach to improve DR diagnosis with limited resources. By adapting existing deep learning models, our research shows significant enhancements in diagnostic accuracy for DR. However, further validation with larger datasets and comparisons with other methods are necessary to confirm our findings. This study contributes to better detection of hard exudates in DR, ultimately leading to improved patient outcomes.