Self-correcting convolution segmentation of infected lung regions algorithm

Abstract

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 introduction of a self-correcting convolution module that improves the receptive field and the diversity of extracted features. Furthermore, the enhanced mDice loss function, integrating segmentation penalties, contributes to improved model performance. This method offers a promising advancement in lung infection segmentation using deep learning techniques.