A hybrid object detection and transformer-based network with dynamic zoom and spatial fusion for segmenting low-contrast images

Accurate segmentation of low-contrast images plays a crucial role in computer-aided diagnosis and treatment, particularly for early lesion detection and clinical decision support. To address the limitations of existing approaches in boundary localisation and multi-scale context modelling, we propose a lightweight and efficient hybrid segmentation framework, referred to as SwinFuseNet. The proposed architecture combines the strengths of detection-based and Transformer-based models. Specifically, the Global Pyramid Attention Backbone Network integrates a shifted window-based Transformer mechanism to enhance the global representation of blurred lesions in low-contrast images. In the feature aggregation stage, two dedicated modules–Dynamic Zoom Fusion and Spatial Interaction Fusion—are introduced to adaptively integrate information from multiple layers, effectively refining local boundary representations and fine-grained structural features. Additionally, a lightweight attention subnetwork is employed to highlight salient regions while suppressing background noise, thereby improving overall segmentation precision. Experiments conducted on four publicly available low-contrast image segmentation datasets (ISIC 2018, PH 2 , LUNA16, Kvasir-SEG and Brisc2025) demonstrate that the proposed method significantly outperforms existing models, including variants of U-Net and a recent detection-based segmentation framework. On the ISIC 2018 dataset, the proposed network achieves a Dice coefficient of 0.9873 and an Intersection-over-Union score of 0.9566, representing improvements of 4.48% and 6.53% respectively over the current state-of-the-art, showing a remarkable 78% and 60% improvement, respectively, toward perfection from the best alternative algorithm. These results confirm the effectiveness and practical relevance of the proposed method in the domain of low-contrast medical image segmentation.