Cyber-Medicine Service for Medical Diagnosis Based on IoT and Cloud Infrastructure

Abbas, Safia

doi:10.1007/978-3-030-44289-7_58

Cited by 4 publications

(11 citation statements)

References 18 publications

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“…DiaMe as an IoMT cloud service has been introduced in [1], [35], it contains various sensors types, connected either end-to-end or remotely, and provide multiple functionalities such as ECG monitoring and disorder detection, alarming for critical cases, tumors detection for kidney and brain, and others. Ensemble technique based on multiple types of classifiers, to improve the interoperable symptoms, is used.…”

Section: Related Workmentioning

confidence: 99%

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Abbas

Mahmoud

2021

IJECE

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Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.

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Section: Related Workmentioning

confidence: 99%

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Abbas

Mahmoud

2021

IJECE

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“…As shown in Figure 1, the service contains three main modules. The following subsections will discuss selected parts of the service, for more details see [27].…”

Section: Diagnostic Service Block Diagrammentioning

confidence: 99%

“…This module considers various input/output data interactions through the whole service, such data is generated from multiple IoT sources and enjoys different structures and types, see [27] for more details. Where, for ECG measuring, it is an optional phase done by a hardware, arduino kit, which was designed and developed, see Figure 2(a), to fit the service as a portable device that can be easily plugged in.…”

Section: Data Provider (Dp) Modulementioning

confidence: 99%

“…CA1 pipeline chart -Digital data: any other data types, rather than analogue and scans, considered digital and manipulated using different classification algorithms. The master classifier agent gathers different digital data from the DP module, and distributes the required tasks between classifiers from CA2 to CA4, for more details see [27].…”

Section: Master Classifier Agent Modulementioning

confidence: 99%

“…If the existed data is not sufficient to extract the required knowledge for the queries, the engine will run a Web crawler to search for the most relevant knowledge required to reply the query. For more details about different phases and interoperability see [27].…”

Section: Inference Modulementioning

confidence: 99%

See 2 more Smart Citations

An innovative IoT service for medical diagnosis

Abbas

2020

IJECE

Self Cite

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Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability.

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Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Abbas

Mahmoud

2020

IJEEI

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Cyber-Medicine Service for Medical Diagnosis Based on IoT and Cloud Infrastructure

Cited by 4 publications

References 18 publications

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

An innovative IoT service for medical diagnosis

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

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