Transparent formulation of explanation methods is essential for elucidating the predictions of neural network models, which are commonly of a black-box nature. Layer-wise Relevance Propagation (LRP) stands out as a well-established method that transparently traces the flow of a model’s prediction backward through its architecture by backpropagating relevance scores. However, LRP has not fully considered the existence of a skip connection, and its application to the widely used ResNet architecture has not been thoroughly explored. In this study, we extend LRP to the ResNet models by introducing relevance splitting at a point where outputs from a skip connection and a residual block converge. Moreover, our formulation ensures that the conservation property is maintained throughout the process, thereby preserving the integrity of the generated explanations. To evaluate the effectiveness of our approach, we conduct the experiments on ImageNet and CUB dataset. Our method demonstrated superior performance compared to baseline methods in standard evaluation metrics such as Insertion-Deletion score while maintaining its conservation property.