Internet of things and deep learning based digital twins for diagnosis of brain tumor by analyzing MRI images

被引：0
作者：
Sultanpure, Kavita A. [1 ]
Bagade, Jayashri [2 ]
Bangare, Sunil L. [3 ]
Bangare, Manoj L. [4 ]
Bamane, Kalyan D. [5 ]
Patankar, Abhijit J. [6 ]
机构：
[1] Department of Information Technology, Vishwakarma Institute of Technology, Savitribai Phule Pune University, Pune
[2] Department of Information Technology, Vishwakarma Institute of Information Technology, Pune
[3] Department of Information Technology, Sinhgad Academy of Engineering, Savitribai Phule Pune University, Pune
[4] Department of Information Technology, Smt. KashibaiNavale College of Engineering, Savitribai Phule Pune University, Pune
[5] Department of Information Technology, D. Y. Patil College of Engineering, Savitribai Phule Pune University, Pune
[6] Department of Information Technology, D. Y. Patil College of Engineering Akurdi, Savitribai Phule Pune University, Pune
来源：
Measurement: Sensors | 2024年 / 33卷
关键词：
Accuracy; Brain tumor detection; Convolutional neural network; Deep learning; Digital twins; Healthcare; 4.0; Particle Swarm Optimization;
D O I：
10.1016/j.measen.2024.101220
中图分类号：
学科分类号：
摘要：
Although brain tumours are few, they have one of the highest mortality rates among all types of cancer due to their abnormal growth and proliferation. Brain tumours develop due to the accumulation of abnormal tissues in the brain. Various forms of abnormal tissue exist, however, in the majority of cases, they develop in a regular manner and perish without creating any detrimental effects. Digital twins are occasionally known as digital mirrors, digital mapping, and digital replicas. All of these are synonymous terms for the identical entity. It is a technique for transferring digital or physical information from one realm to another. Image processing involves enhancing or eliminating data from a photograph to achieve a certain objective. Convolutional neural networks are a specific type of neural network that take signals from images as input and produce the image itself or a subset of its elements as output. This research presents a technique for identifying brain cancers using digital replicas and advanced machine learning algorithms by analysing MRI images. Images obtained from MRI machines are stored in a centralised cloud using Internet of Things (IoT) digital devices. The input pictures and other health-related data are then retrieved from cloud storage. The Particle Swarm Optimization approach chooses features. Brain tumor images are classified using machine learning techniques such as convolutional neural networks, support vector machines, and extreme learning machines. The CNN algorithm demonstrates greater accuracy when assessing MRI images for the purpose of identifying brain tumours. © 2024 The Authors
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  • [1] Aheleroff S., Polzer J., Huang H., Zhu Z., Tomzik D., Lu Y., Et al., Smart Manufacturing Based on Digital Twin Technologies. Industry 4.0: Challenges, Trends, and Solutions in Management and Engineering, pp. 77-82, (2020)
  • [2] Alzubi J.A., Alzubi O.A., Singh A., Ramachandran M., Cloud-IIoT based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning, IEEE Trans. Ind. Inf., 19, (2022)
  • [3] Raghuvanshi A., Singh U., Joshi C., A review of various security and privacy innovations for IoT applications in healthcare, Adv. Healthc. Syst., pp. 43-58, (2022)
  • [4] Jasti V., Zamani A., Arumugam K., Naved M., Pallathadka H., Sammy F., Et al., Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis, Secur. Commun. Network., 2022, pp. 1-7, (2022)
  • [5] Ogino M., Kanoga S., Ito S.I., Mitsukura Y., Semi-supervised learning for auditory event-related potential-based brain–computer interface, IEEE Access, 9, pp. 47008-47023, (2021)
  • [6] SushovanChaudhury A.N.K., Gupta S., SakthidasanSankaran K., Khan S., KartikSau A., Sammy F., Effective image processing and segmentation-based machine learning techniques for diagnosis of breast cancer, Comput. Math. Methods Med., 2022, (2022)
  • [7] Bhanumurthy M.Y., Anne K., An Automated Segmentation of Brain MRI for Detection of Normal Tissues Using Improved Machine Learning Approach, (2015)
  • [8] Liu J., Guo L., A new brain MRI image segmentation strategy based on K-means clustering and SVM, 7th Int. Conf. Intell. Human-Machine Syst. Cybern., 2, pp. 270-273, (2015)
  • [9] OmneyaAttallah H., Sharkas M.A., Detecting and Classifying Fetal Brain Abnormalities Using Machine Learning Techniques, pp. 1371-1376, (2018)
  • [10] Deepak K.S., Gokul K., Hinduja R., Rajkumar S., An Efficient Approach to Predict Tumor in 2D Brain Image Using Classification Techniques, pp. 559-564, (2013)
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