Choudhary Shilpa, Kumar Sandeep, Siddhaarth Pammi Sri, Charitasri Guntu, Gulhane Monali, Rakesh Nitin, Mon Feslin Anish, Al-Rasheed Amal, Getahun Masresha, Soufiene Ben Othman
Department of CSE (AIML), Neil Gogte Institute of Technology, Hyderabad, India.
School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371, India.
BMC Med Inform Decis Mak. 2025 Jun 4;25(1):207. doi: 10.1186/s12911-025-03027-2.
The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification has a high clinical relevance in these patients because this would help to prevent false-negative diagnosis and to treat them in a timely and effective manner, thus reducing their clinical impacts.Our research aims to automate the process and eliminate manual efforts in blood cell counting. While our primary focus is on detection and classification, the output generated by our approach can be useful for disease prediction. This follows a two-step approach, where YOLO-based detection is first performed to locate blood cells, followed by classification using a hybrid CNN model to ensure accurate identification. We conducted a thorough and extensive comparison with other state-of-the-art models, including MobileNetV2, ShuffleNetV2, and DarkNet, for blood cell detection and classification. In terms of real-time performance, YOLOv10 outperforms other object detection models with better detection rates and classification accuracy. But MobileNetV2 and ShuffleNetV2 are more computationally efficient, which becomes more appropriate for resource-constrained environments. In contrast, DarkNet outperformed in terms of feature extraction performance, and the fine blood cell type classification. Additionally, an annotated blood cell data set was generated for this study. A diverse set of blood cell images with fine-grained annotations is contained in this dataset to make it useful for deep learning models training and evaluation. Because the present dataset will be an important resource for researchers and developers working on automatic blood cell detection and classification systems, we will make it publicly available under the open-access nature in order to accelerate the collaboration and progress in this field.
血细胞的检测和分类对于诊断和监测各种血液相关疾病至关重要，如贫血、白血病和感染，所有这些疾病都可能导致显著的死亡率。准确的血细胞识别在这些患者中具有很高的临床相关性，因为这有助于防止漏诊，并及时有效地对他们进行治疗，从而降低其临床影响。我们的研究旨在实现血细胞计数过程的自动化，消除人工操作。虽然我们的主要重点是检测和分类，但我们方法产生的输出可用于疾病预测。这采用了两步法，首先基于YOLO进行检测以定位血细胞，然后使用混合CNN模型进行分类以确保准确识别。我们与其他先进模型，包括MobileNetV2、ShuffleNetV2和DarkNet，就血细胞检测和分类进行了全面而广泛的比较。在实时性能方面，YOLOv10在检测率和分类准确率方面优于其他目标检测模型。但MobileNetV2和ShuffleNetV2计算效率更高，更适合资源受限的环境。相比之下，DarkNet在特征提取性能和精细血细胞类型分类方面表现出色。此外，本研究生成了一个带注释的血细胞数据集。该数据集中包含了一组多样化的具有细粒度注释的血细胞图像，使其对深度学习模型的训练和评估有用。由于当前数据集将成为从事自动血细胞检测和分类系统研究的人员和开发人员的重要资源，我们将在开放获取的性质下将其公开，以加速该领域的合作和进展。
