AN IMPROVED ATTENTION-GUIDED MULTI-SCALE U-NET ARCHITECTURE FOR PULMONARY NODULE SEGMENTATION

Abstract

Segmentation of pulmonary nodules is one of the key processes of computer-aided diagnosis of lung cancer, as it has a direct impact on early diagnosis, assessment of the disease, and clinical decision-making. Computer-assisted segmentation of computed tomography images is still a formidable task because of the marked heterogeneity of nodule size, abnormal morphology, low contrast, and complicated anatomic attachments. The traditional U-Net-based deep learning models show good performance, albeit with drawbacks in that they lack the ability to capture finer boundary information, and at the same time, they are not good at maintaining a global contextual understanding. The chapter of this chapter introduces a better attention-directed multi-scale U-Net architecture to deal with these issues using more representative features and selective integration of information. The suggested architecture incorporates multi-scale feature aggregation and fine-tuned attention-based skip connections to prioritize anatomically important areas and avoid interference from the background. Experimental analysis in benchmark lung CT datasets proves significant results of improvement of the accuracy of segmentation, boundary delineation, and strength in comparison with traditional U-Net and attention-based variants. The given architecture can ensure high-quality segmentation of heterogeneous nodules with the characteristics and ensures a high level of computational efficiency that is appropriate to the clinical applications. This paper offers a deep learning architecture that is strong and scalable in the segmentation of pulmonary nodules and provides a valuable input to the optimization of medical image management and computer-aided diagnosis software in the future.