This paper introduces a novel self-correcting convolution module that significantly differs from traditional convolution techniques by enabling multi-scale feature extraction through heterogeneous kernel processing and feature calibration. The module uniquely combines down-sampling and up-sampling operations within a single convolution block to capture both local and global contexts, addressing key limitations in existing methods. In addition, an improved Dice loss function is proposed, which integrates both under- and over-segmentation penalties through an mDice loss. This is combined with a cross-entropy loss based on classification to optimize segmentation performance. The proposed self-correcting convolution segmentation algorithm demonstrates superior accuracy in segmenting lung infection regions compared to existing methods, particularly the AMSU-Net network. Experimental results indicate that the inclusion of multi-scale spatial information and refined loss functions significantly enhances segmentation precision. The novelty of this research lies in the