A new accuracy-aware extension to LRP-based pruning targets convolutional neural networks in data-scarce transfer learning. This method stops cascading accuracy degradation by refining how weights are removed. It allows developers to compress models without sacrificing performance on small datasets. This incremental improvement optimizes efficiency for specialized vision tasks where training data is limited.