Naouali Sami, El Othmani Oussama
Information Systems Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia.
Information Systems Department, Military Academy of Fondouk Jedid, Nabeul 8012, Tunisia.
J Imaging. 2025 May 16;11(5):157. doi: 10.3390/jimaging11050157.
Hematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settings. In this study, we present an AI-driven system for automated blood cell anomaly detection, combining computer vision and machine learning models to support efficient diagnostics in hematology and telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, and zero-shot learning to identify and categorize cell types and abnormalities from blood smear images. Evaluated on real annotated samples, the system achieved high performance, with a precision of 0.98, recall of 0.99, and F1 score of 0.98. These results highlight the potential of the proposed system to enhance remote diagnostic capabilities and support clinical decision making in underserved regions.
血液学在诊断和管理各种血液相关疾病中起着关键作用。然而，人工解读血涂片图像既耗时，又高度依赖专家的可用性。此外，在偏远和资源有限的地区，这尤其具有挑战性。在本研究中，我们提出了一种人工智能驱动的系统，用于自动检测血细胞异常，该系统结合了计算机视觉和机器学习模型，以支持血液学和远程医疗环境中的高效诊断。我们的架构集成了分割（YOLOv11）、分类（ResNet50）、迁移学习和零样本学习，以从血涂片图像中识别细胞类型并对异常进行分类。在真实标注样本上进行评估时，该系统表现出色，精确率为0.98，召回率为0.99，F1分数为0.98。这些结果凸显了所提出系统在增强偏远地区诊断能力以及支持临床决策方面的潜力。
