利用紫外可见光谱和 PSO 优化的 1D-CNN 模型进行血液 CO 状态分类

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pertanika Journal of Science and Technology Pub Date : 2024-07-16 DOI:10.47836/pjst.32.4.02
A. Huong, Kim Gaik Tay, Kok Beng Gan, X. Ngu
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引用次数: 0

摘要

快速有效的血液一氧化碳(CO)评估非常重要,尤其是在估算与一氧化碳相关的发病率和制定有效的预防措施方面。传统的一氧化碳呼气分析检测方法灵敏度不高,而采集生物液体样本进行一氧化碳含量测量容易受到外界污染,且成本高昂,无法频繁使用。本研究提出了一种由三个堆叠双卷积层组成的一维卷积神经网络(1D-CNN),利用漫反射光谱技术对血液中的 CO 状态进行二元分类。迭代粒子群优化(PSO)有效地找到了最佳网络参数,以从反射光谱数据中学习重要特征。研究结果表明,在使用所提出的 CNN 网络判断吸烟者血液中 CO 值异常方面,测试准确性、特异性和精确性分别为 92.9%、90% 和 89.7%,灵敏度高达 96.3%。与现有的八个机器学习和深度学习模型进行比较后发现,所提出的方法在对血液一氧化碳状态进行分类方面非常有效,同时将计算时间减少了 8-13 倍。这项工作的发现为神经网络设计自动化、医疗保健管理和皮肤相关研究领域的研究人员提供了新的见解,特别是在无损评估和临床决策中的应用。
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Blood CO Status Classification Using UV-VIS Spectroscopy and PSO-optimized 1D-CNN Model
Rapid and effective blood carbon monoxide (CO) assessment is of great importance, especially in estimating CO-related morbidity and instituting effective preventive measures. The conventional detection methods using CO breath analysis lack sensitivity, while collecting biological fluid samples for CO level measurement is prone to external contamination and expensive for frequent use. This study proposes a one-dimensional convolutional neural network (1D-CNN) consisting of three stacked biconvolutional layers for binary classification of blood CO status using the diffuse reflectance spectroscopy technique. Iterative particle swarm optimization (PSO) has efficiently found the best network parameters to learn important features from the reflectance spectroscopy data. The findings showed good testing accuracy, specificity, and precision of 92.9%, 90%, and 89.7%, respectively, and a high sensitivity of 96.3% in determining abnormal blood CO among smokers using the proposed CNN network. Comparisons with eight existing machine learning and deep learning models revealed the proposed method’s effectiveness in classifying blood CO status while reducing computing time by 8–13 folds. The findings of this work provide new insights that are valuable for researchers in neural network design automation, healthcare management, and skin-related research, specifically for application in nondestructive evaluation and clinical decision-making.
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来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
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