"Cell-level Image Patches of Blood Smear"

"Iron deficiency anemia (IDA) and thalassemia (THL) are common hematologic disorders requiring efficient and accurate screening for early diagnosis. Traditional blood smear analysis is labor-intensive and subjective, underscoring the need for AI-driven solutions to enhance Sensitivity and Specificity.This study introduces a novel hybrid AI-based patient-level classification framework integrating soft voting with optimized probability-based thresholds. The model was trained and validated using a real-world dataset from Hatyai Hospital, Thailand, and evaluated at both the patch and patient levels. The proposed approach achieved 96% accuracy on the test set, with precision-recall values of 1.00 and 0.83 for IDA and 0.95 and 1.00 for THL, respectively. THL sensitivity reached 1.00 at the patient level, while IDA sensitivity was 0.83. Bayesian probability updates confirmed prediction reliability, yielding post-test probabilities exceeding 99.99% for IDA and 80% for THL. The model explained 62.84% of the variance in patient classifications, demonstrating strong discriminatory power.Model interpretability was assessed using SHAP and Grad-CAM, highlighting key red blood cell morphological features. The proposed framework offers a cost-effective and clinically interpretable AI-assisted hematology screening tool, enhancing decision support in resource-limited settings.Limitations include the single-center dataset and the need for adaptive threshold optimization. Future work will focus on multi-center validation and integration into real-world clinical workflows. This study establishes a structured baseline for AI-assisted hematology screening, supporting early disease detection and improved clinical decision-making.  "