Large Intelligent Surfaces (LISs) have emerged as a promising technology for enhancing spectral efficiency and communication capacity in the Sixth Generation of Cellular Com-munications (6G). Low-complexity receiver architectures for LISs rely on Maximum Ratio Combining (MRC) and Equal Gain Combining (EGC) receivers, often complemented by it-erative detection techniques for interference mitigation. In this work, we propose a novel approach where a neural network replaces iterative interference cancellation, learning to estimate the transmitted signals directly from the received data, mitigating interference without requiring iterative cancellation. Moreover, this also eliminates the need for chan-nel matrix inversion at each frequency component, as required for Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE) receivers, reducing computational complexity while still achieving a good performance improvement. The neural network parameters are optimized to balance performance and computational cost.