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IOTEML：一种基于物联网 (IoT) 的增强机器学习模型，用于肿瘤研究。

IOTEML: An Internet of Things (IoT)-Based Enhanced Machine Learning Model for Tumour Investigation.

机构信息

Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, Tamil Nadu 602105, India.

Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, Durg, Chhattisgarh 491001, India.

出版信息

Comput Intell Neurosci. 2022 Sep 14;2022:1391340. doi: 10.1155/2022/1391340. eCollection 2022.

DOI:10.1155/2022/1391340
PMID:36156969
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9492353/
Abstract

In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of 1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.

摘要

在当今科技时代，人体的各种疾病也在不断增加。在体内引起更多不适的肿瘤将增加大多数患者的不适。患者根据肿瘤大小和类型会有不同的反应。医疗领域的未来发展方向是基于物联网设备开发工具。这些进步将来会遵循基于人工智能开发的多种机器学习的特殊功能。按照这个顺序，本文提出了一种名为基于物联网的增强机器学习的改进算法。它的特别之处在于它涉及单独的功能来诊断每种类型的肿瘤。它分析和计算肿瘤的大小、形状和位置等。癌症的治愈取决于我们发现癌症的阶段。癌症的早期发现有快速治愈的潜力。在饱和点，提出的基于物联网的增强机器学习模型的准确率达到 94.56%，精度达到 94.12%，召回率达到 94.98%，1 分率达到 95.12%，执行时间为 1856 毫秒。进行了模拟以测试模型的功效，模拟结果表明，与其他方法相比，基于物联网的增强机器学习获得了更高的智能率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/1b2571fc33dd/CIN2022-1391340.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/c6eaeaadf52d/CIN2022-1391340.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/bb43c9341c75/CIN2022-1391340.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/b361f631b3b0/CIN2022-1391340.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/cf512e1e7357/CIN2022-1391340.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/6e208b5698ca/CIN2022-1391340.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/f33fc59f0d55/CIN2022-1391340.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/1b2571fc33dd/CIN2022-1391340.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/c6eaeaadf52d/CIN2022-1391340.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/cd825c86efa7/CIN2022-1391340.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/bb43c9341c75/CIN2022-1391340.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/34c7f47b22ee/CIN2022-1391340.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/b361f631b3b0/CIN2022-1391340.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/cf512e1e7357/CIN2022-1391340.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/6e208b5698ca/CIN2022-1391340.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/f33fc59f0d55/CIN2022-1391340.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be72/9492353/1b2571fc33dd/CIN2022-1391340.alg.001.jpg

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本文引用的文献

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Multifractal texture estimation for detection and segmentation of brain tumors.多分辨纹理估计在脑肿瘤检测和分割中的应用。
IEEE Trans Biomed Eng. 2013 Nov;60(11):3204-15. doi: 10.1109/TBME.2013.2271383. Epub 2013 Jun 27.