Berghout Tarek
Laboratory of Automation and Manufacturing Engineering, Department of Industrial Engineering, Batna 2 University, Batna 05000, Algeria.
J Imaging. 2024 Oct 2;10(10):245. doi: 10.3390/jimaging10100245.
Anemia diagnosis is crucial for pediatric patients due to its impact on growth and development. Traditional methods, like blood tests, are effective but pose challenges, such as discomfort, infection risk, and frequent monitoring difficulties, underscoring the need for non-intrusive diagnostic methods. In light of this, this study proposes a novel method that combines image processing with learning-driven data representation and model behavior for non-intrusive anemia diagnosis in pediatric patients. The contributions of this study are threefold. First, it uses an image-processing pipeline to extract 181 features from 13 categories, with a feature-selection process identifying the most crucial data for learning. Second, a deep multilayered network based on long short-term memory (LSTM) is utilized to train a model for classifying images into anemic and non-anemic cases, where hyperparameters are optimized using Bayesian approaches. Third, the trained LSTM model is integrated as a layer into a learning model developed based on recurrent expansion rules, forming a part of a new deep network called a recurrent expansion network (RexNet). RexNet is designed to learn data representations akin to traditional deep-learning methods while also understanding the interaction between dependent and independent variables. The proposed approach is applied to three public datasets, namely conjunctival eye images, palmar images, and fingernail images of children aged up to 6 years. RexNet achieves an overall evaluation of 99.83 ± 0.02% across all classification metrics, demonstrating significant improvements in diagnostic results and generalization compared to LSTM networks and existing methods. This highlights RexNet's potential as a promising alternative to traditional blood-based methods for non-intrusive anemia diagnosis.
贫血诊断对儿科患者至关重要，因为它会影响生长发育。传统方法，如血液检测，虽有效但存在挑战，如不适、感染风险和频繁监测困难，这凸显了对非侵入性诊断方法的需求。有鉴于此，本研究提出了一种新颖的方法，该方法将图像处理与学习驱动的数据表示及模型行为相结合，用于儿科患者的非侵入性贫血诊断。本研究的贡献有三个方面。首先，它使用图像处理管道从13个类别中提取181个特征，并通过特征选择过程确定学习中最关键的数据。其次，利用基于长短期记忆（LSTM）的深度多层网络训练一个模型，将图像分类为贫血和非贫血病例，其中使用贝叶斯方法优化超参数。第三，将训练好的LSTM模型作为一层集成到基于循环扩展规则开发的学习模型中，形成一个名为循环扩展网络（RexNet）的新深度网络的一部分。RexNet旨在学习类似于传统深度学习方法的数据表示，同时理解因变量和自变量之间的相互作用。所提出的方法应用于三个公共数据集，即6岁以下儿童的结膜眼图像、手掌图像和指甲图像。RexNet在所有分类指标上的总体评估为99.83±0.02%，与LSTM网络和现有方法相比，诊断结果和泛化能力有显著提高。这突出了RexNet作为传统基于血液的方法用于非侵入性贫血诊断的有前途替代方法的潜力。
