Alsamri Jamal, Alqahtani Hamed, Al-Sharafi Ali M, Darem Abdulbasit A, Nazim Khalid, Sattar Abdul, Alshammeri Menwa, Alzahrani Ahmad A, Obayya Marwa
Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, King Khalid University, Abha, Saudi Arabia.
Sci Rep. 2025 Apr 12;15(1):12548. doi: 10.1038/s41598-025-85815-4.
Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease. With the growth of technology, patient-related information is attained rapidly and in big sizes. Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. The main aim of the CADHDD-SFLNHM approach is to enhance the detection and classification of haematologic disorders. At first, the Sobel filter (SF) technique is utilized for preprocessing to improve the quality of blood cell images. Additionally, the modified LeNet-5 model is used in the feature extractor process to capture the essential characteristics of blood cells relevant to disorder classification. The convolutional neural network and bi-directional gated recurrent unit with attention (CNN-BiGRU-A) method is employed to classify and detect haematologic disorders. Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. The experimental result analysis of the CADHDD-SFLNHM model was accomplished using a benchmark database. The performance validation of the CADHDD-SFLNHM model portrayed a superior accuracy value of 97.91% over other techniques.
分析生物医学图像对于实现高性能成像和众多医学应用至关重要。确定疾病的分析是治疗患者的关键阶段。同样，血液检测的统计值、患者的个人数据以及专家评估对于诊断疾病也是必不可少的。随着技术的发展，与患者相关的信息能够快速且大量地获取。目前，存在多种物理方法利用白细胞（WBC）图像的微观健康信息来评估和预测血癌，这些信息对于预测很稳定且会导致许多死亡。机器学习（ML）和深度学习（DL）有助于数据中模式的分类和收集，尤其是在众多血液学领域所采用的人工智能方法的发展中。本研究提出了一种基于混合模型空间特征学习网络的血液系统疾病检测计算机辅助诊断新方法（CADHDD - SFLNHM），该方法使用血细胞图像。CADHDD - SFLNHM方法的主要目的是提高血液系统疾病的检测和分类能力。首先，利用Sobel滤波器（SF）技术进行预处理以提高血细胞图像的质量。此外，在特征提取过程中使用改进的LeNet - 5模型来捕捉与疾病分类相关的血细胞基本特征。采用卷积神经网络和带注意力的双向门控循环单元（CNN - BiGRU - A）方法对血液系统疾病进行分类和检测。最后，CADHDD - SFLNHM模型采用鹈鹕优化算法（POA）方法对CNN - BiGRU - A方法中涉及的超参数进行微调。使用基准数据库完成了CADHDD - SFLNHM模型的实验结果分析。CADHDD - SFLNHM模型的性能验证显示，其准确率高达97.91%，优于其他技术。
