Ramzan Muhammad, Sheng Jinfang, Saeed Muhammad Usman, Wang Bin, Duraihem Faisal Z
School of Computer Science and Engineering, Central South University, Changsha, 410017, Hunan, China.
Department of Mathematics, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia.
Vis Comput Ind Biomed Art. 2024 Jul 17;7(1):18. doi: 10.1186/s42492-024-00169-4.
This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML - particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors - in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components - including the blue-green-red, multiple, and spatial attentions - in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.
本研究探讨了使用机器学习（ML）技术进行贫血检测的关键问题。贫血是一种广泛存在且对健康有重大影响的血液疾病，但往往未被检测出来。这就需要及时且高效的诊断方法，因为依赖人工评估的传统方法既耗时又主观。本研究探索了ML的应用——特别是分类模型，如逻辑回归、决策树、随机森林、支持向量机、朴素贝叶斯和k近邻——结合包含注意力模块和空间注意力的创新模型来检测贫血。所提出的模型展示了有前景的结果，在文本和图像数据集上均实现了高精度、精确率、召回率和F1分数。此外，发现一种结合文本和图像数据的综合方法优于单独的模态。具体而言，所提出的AlexNet多重空间注意力模型实现了99.58%的卓越准确率，凸显了其革新贫血自动检测的潜力。消融研究的结果证实了关键组件——包括蓝绿红、多重和空间注意力——在提升模型性能方面的重要性。总体而言，本研究为无创贫血检测提出了一个全面且创新的框架，为该领域贡献了有价值的见解。
