Large Data Requirements: Deep learning models require vast amounts of labeled data to achieve high accuracy. The more data the system has access to, the better it can learn complex patterns.

Deep learning (DL) methods offer a promising solution by reconstructing raw, low-quality data into high-resolution images, helping to preserve diagnostic accuracy even under time constraints.