The DeepLabV3+ Algorithm Combined With the ResNeXt Network for Medical Image Segmentation

Abstract

This paper presents a semantic segmentation algorithm for medical images, leveraging the DeepLabV3+ architecture in conjunction with the ResNeXt network. The proposed algorithm takes into account the correlation between each structure of lung images and the unique characteristics of image features. Firstly, the cavity convolution algorithm is employed to enhance the receptive field of the network's feature map without augmenting the number of network parameters. Then, the extraction of dense pixel features and the expansion of the receptive field for lung images are conducted using a Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP) module integrated with the ResNeXt network, which is based on multi-scale feature fusion. This ultimately leads to improved refinement of the edges in segmented lung images. The algorithm has shown excellent performance in clinical applications, providing medical professionals with more precise and accurate data to inform diagnostic and treatment strategies. Our algorithm achieved Mean Pixel Accuracy (MPA) of 0.9866, Intersection Over Union (IOU) of 0.9886 and Mean Intersection over Union (MIoU) of 0.9761, which demonstrates superiority over other state-of-the-art algorithms.