Enhancing White Blood Cell Segmentation with SE-UNet: A Channel-Wise Attention Approach for Improved Precision in Blood Smear Analysis

White Blood Cells (WBC) play a crucial role in the immune system, and abnormalities in their count, shape, or distribution can indicate various diseases, including infections, autoimmune disorders, and leukemia. Accurate WBC segmentation is essential for quantifying various features of WBCs, such as size, shape, texture, and distribution, which are used as diagnostic indicators by computer-aided systems. However, accurate WBC segmentation from microscopic blood smear images is tedious, even for contemporary deep-learning models. This manuscript proposes an encoder-decoder-based model named SE-UNet, which combines the power of the squeeze-and-excitation (SE) module with the baseline UNet. Adding Squeeze and Excitation modules in the encoder section of U-Net enhances segmentation by incorporating channel-wise attention, extracting more prominent features from whole blood microscopic images, and aiding generalization without significantly increasing computational complexity. The proposed model achieves a 96.5% (0.965) precision value, 93.9% (0.939) recall value, and 95.2% (0.952) F1-score value for the WBC segmentation task on a private dataset obtained from AIIMS Patna. The SE-UNet outperforms the baseline U-Net by about 1.2% in terms of F1 score.