Image segmentation plays a critical role in computer vision applications such as biometric verification, medical imaging, and automated surveillance. However, existing segmentation methods struggle in challenging lighting conditions, where illumination variations introduce distortions. To address this issue, we propose Segmentation with Channel and Spatial Attention - Palmprints (SegCSA-X), a novel deep learning-based segmentation framework designed to enhance robustness against illumination-induced artifacts. The model integrates channel and spatial attention mechanisms to improve feature extraction and segmentation accuracy. SegCSA-X is evaluated against the Segment Anything Model (SAM) using three publicly available palmprint datasets: CASIA, REST, and IITD. While both models perform well on CASIA, SegCSA-X achieves superior results with an IoU of 98.88% and Dice Score of 99.44%. On the more challenging REST and IITD datasets, which contain significant illumination variations, SegCSA-X significantly outperforms SAM, achieving IoU scores of 96.47% (IITD) and 97.22% (REST), and Dice scores of 98.20% (IITD) and 98.58% (REST). These results demonstrate that SegCSA-X establishes a new benchmark for palmprint segmentation, particularly in adverse lighting conditions.