Year: 2025
Type: article
Abstract: ABSTRACT In the field of oncology, lung cancer is a leading contributor to cancer‐related mortality, highlighting the need for early detection of lung nodules for effective intervention. However, accurate segmentation of lung nodules in Computed Tomography (CT) images remains a significant challenge due to issues such as heterogeneous nodule dimensions, low contrast, and their visual similarity with surrounding tissues. To address these challenges, this study proposes the Edge‐Enhanced Feature Pyramid SwinUNet (EE‐FPS‐UNet), an advanced segmentation model that integrates a modified Swin Transformer with a feature pyramid network (FPN). The research objective is to enhance boundary delineation and multi‐scale feature aggregation for improved segmentation performance. The proposed model uses the Swin Transformer to capture long‐range dependencies and integrates an FPN for robust multi‐scale feature aggregation. Its window‐based self‐attention mechanism also reduces computational complexity, making it well‐suited for high‐resolution CT images. Additionally, an edge detection module enhances segmentation by providing edge‐related features to the decoder, improving boundary precision. A comparative analysis evaluates the EE‐FPS‐UNet against leading models, including PSPNet, U‐Net, Attention U‐Net, and DeepLabV3. The results demonstrate that the proposed model outperforms these models, achieving a Dice Similarity of 0.91 and a sensitivity of 0.89, establishing its efficacy for lung nodule segmentation.
Source: IET Image Processing
DOI: 10.1049/ipr2.70072
Institutions: GMR Institute of Technology, King Saud University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,740
Funder: King Saud University
Award: G6716034574
OpenAlex ID: W4409461573