SHIELD-Net: A Sperm Halo Image Classification Framework via Hybrid ROI Extraction and ROI-Guided Dual-View Learning

The sperm chromatin dispersion (SCD) test is widely used to assess sperm DNA fragmentation by observing the halo structure around the sperm head. However, manual halo interpretation is labor-intensive and subjective, which limits its consistency and throughput in clinical practice. To address these limitations, we propose SHIELD-Net, an image-level single-sperm halo morphology classification framework for four halo categories: large halo, medium halo, small halo, and without halo. SHIELD-Net includes two modules, i.e., a hybrid ROI extraction (HyREx) module and dual-view learning with ROI-guided gate fusion (RGF). The HyREx module converts the traditional direct classification pipeline into a segmentation-classification pipeline. It uses the cell segmentation foundation model, i.e., Cellpose, to segment halo-centered regions of interest (ROIs) and proposes a fallback mechanism to ensure segmentation effectiveness. In this way, HyREx can construct a head–halo-centered ROI and reduce interference from tail and background regions. Based on the original image and the extracted ROI, SHIELD-Net performs dual-view ConvNeXt encoding. RGF further uses the ROI representation to generate a spatial gate that recalibrates the original-view feature map toward halo-morphology-related responses. Experiments were conducted on a public expert-annotated sperm microscopy dataset containing 403 individual sperm halo images using stratified five-fold cross-validation. SHIELD-Net achieved a Macro F1 of 0.9290 ± 0.0161. Compared with the original-image ConvNeXt-Small baseline, SHIELD-Net improved Macro F1 by 0.1755. These results indicate that combining head–halo-centered ROI construction with ROI-guided feature recalibration improves image-level sperm halo morphology classification.