IoMT Technology as the Basis of Wearable Online Monitors for Space Distributed Monitoring Systems for Pregnant Women

Yuldashev, Z. M.; Sergeev, A.; Nastueva, Natalia

doi:10.1109/weconf51603.2021.9470556

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…With the management of Internet of things (IoT) devices [2], especially Internet of medical things (IoMT), patients' information could be stolen by the attackers using botnet [3]. Thus, the privacy of IoMT device is indispensable [4,5]. The brain is considered as the primary organ, take the responsibility for emotions, memory, motor skills, vision, respiration, reactions, and many other regulating functions of the human body.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

Rajathi¹,

Kumar²,

Ravikumar³

et al. 2023

Computer Systems Science and Engineering

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Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides, adaptive window filtering (AWF) based image preprocessing is used to remove noise. In addition, the cloud server executes the disease diagnosis model which includes the sparrow search algorithm (SSA) with GoogleNet (SSA-GN) model. The IoMTC-HDBT model applies functional link neural network (FLNN), which has the ability to detect and classify the MRI brain images as normal or abnormal. It finds useful to generate the reports instantly for patients located in remote areas. The validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is carried out interms of sensitivity, accuracy, and specificity. The experimentation outcome pointed out the betterment of the proposed model with the accuracy of 0.984.

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