LAUSR

Sickle cell disease (SCD) is a genetic blood disorder causing red blood cells to deform into a sickle shape, often leading to misdiagnosis. Early detection is crucial, but traditional screening is slow and labor‐intensive. This paper introduces an intelligent microscope system for automated SCD screening, reducing manual intervention. The system uses an interferometric method to capture high‐resolution 3D phase images, combined with a deep learning‐based UNET model for semantic segmentation of sickle and healthy cells. Various machine‐learning models classify RBCs, with the Gradient boosting model achieving 94.9% accuracy. The system is scalable, user‐friendly, and well suited for resource‐limited settings, offering a faster, more reliable diagnostic tool. This innovation not only improves SCD detection but also sets the stage for AI‐driven haematological diagnostics. Future advancements will enhance system robustness and undergo extensive clinical validation.