A cancer diagnosis transformer model based on medical IoT data for clinical measurements in predictive care systems.

IF 2.2 4区 工程技术 Q3 PHARMACOLOGY & PHARMACY
Bioimpacts Pub Date : 2024-12-04 eCollection Date: 2025-01-01 DOI:10.34172/bi.30640
Panpan Li, Yan Lv, Haiyan Shang
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引用次数: 0

Abstract

Introduction: In recent years, advancements in information and communication technology (ICT) and the internet of things (IoT) have revolutionized the healthcare industry, enabling the collection, analysis, and utilization of medical data to improved patient care. One critical area of focus is the development of predictive care systems for early diagnosis and treatment of cancer and disease.

Methods: Leveraging medical IoT data, this study proposes a novel approach based on transformer model for disease diagnosis. In this paper, features are first extracted from IoT images using a transformer network. The network utilizes a convolutional neural network (CNN) in the encoder part to extract suitable features and employs decoder layers along with attention mechanisms in the decoder part. In the next step, considering that the extracted features have high dimensions and many of these features are irrelevant and redundant, relevant features are selected using the Harris hawk optimization algorithm.

Results: Various classifiers are used to label the input data. The proposed method is evaluated using a dataset consisting of 5 classes for testing and evaluation, and all results are provided into tables and plots.

Conclusion: The experimental results demonstrate that the proposed method acceptable performance compared to other methods.

基于医疗物联网数据的癌症诊断变压器模型,用于预测护理系统的临床测量。
近年来,信息通信技术(ICT)和物联网(IoT)的进步彻底改变了医疗保健行业,使医疗数据的收集、分析和利用能够改善患者的护理。重点关注的一个关键领域是开发用于癌症和疾病早期诊断和治疗的预测性护理系统。方法:利用医疗物联网数据,提出一种基于变压器模型的疾病诊断新方法。在本文中,首先使用变压器网络从物联网图像中提取特征。该网络在编码器部分使用卷积神经网络(CNN)提取合适的特征,在解码器部分使用解码器层和注意机制。下一步,考虑到提取的特征维数较高,且其中很多特征是不相关和冗余的,使用Harris hawk优化算法选择相关特征。结果:使用各种分类器对输入数据进行标记。使用包含5个类别的数据集对所提出的方法进行测试和评估,并将所有结果以表格和图的形式提供。结论:实验结果表明,与其他方法相比,该方法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioimpacts
Bioimpacts Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
4.80
自引率
7.70%
发文量
36
审稿时长
5 weeks
期刊介绍: BioImpacts (BI) is a peer-reviewed multidisciplinary international journal, covering original research articles, reviews, commentaries, hypotheses, methodologies, and visions/reflections dealing with all aspects of biological and biomedical researches at molecular, cellular, functional and translational dimensions.
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