IoMT-based computational approach for detecting brain tumor

Khan, Shah Rukh; Sikandar, Misba; Almogren, Ahmad; Din, Ikram Ud; Guerrieri, Antonio; Fortino, Giancarlo

doi:10.1016/j.future.2020.03.054

Cited by 62 publications

(25 citation statements)

References 20 publications

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“…Reference works [12][13][14][15][16][17] show the applications of AI/ML for the e-healthcare domain. In [12], ML and AI solutions are used to augment the diagnosis and screening process of the identified COVID-19 patients with the help of "radio imaging" technology which works on similar methodology as "computed tomography (CT)", X-Ray, and blood sample data.…”

Section: Related Workmentioning

confidence: 99%

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Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Razdan

Sharma

2021

IETE Technical Review

188

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In the Internet of Medical Things (IoMT), the Internet of Things (IoT) is integrated with medical devices, enabling improved patient comfort, cost-effective medical solutions, quick hospital treatments, and even more personalized healthcare. The paper first provides the introduction of IoMTs and then introduces an architecture of IoMTs. Later, it provides the current operations of the healthcare system and discusses the mapping of these operations into the architectural diagram. Further, several emerging technologies such as Physically Unclonable Functions (PUF), Blockchain, Artificial Intelligence (AI), and Software-Defined Networking (SDN) are envisioned as important technologies to overcome several challenges in e-healthcare such as security, privacy, accuracy, and performance. Finally, we provide three case studies for IoMT based on -(1) PUF-based Authentication, (2) AIenabled SDN Assisted e-healthcare, and (3) Blockchain Assisted Patient Centric System. The solutions presented in this paper may have a huge impact on the speed at which IoMT infrastructure can efficiently evolve with market evolution.

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Section: Related Workmentioning

confidence: 99%

“…The problem with this research is that only two data sets were used to measure the accuracy of the method. In [15], several ML algorithms, association rule mining, i.e. Partial Tree (PART), Random Forest, Naive Bayes, and Random Tree are compared to detect brain tumour from the Magnetic resonance (MR) images.…”

Section: Related Workmentioning

confidence: 99%

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Razdan

Sharma

2021

IETE Technical Review

188

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“…It should be noted that, continual monitoring allows for detecting infrequent flares of symptoms which may not be feasible based on infrequent discrete visits (Joyia et al, 2017 ; Khan et al, 2020 ). This is a major benefit of IoMT technologies which can significantly help with the fight against a pandemic, if low-cost, and highly-accessible wearable IoMT can be made available.…”

Section: Introductionmentioning

confidence: 99%

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19

2021

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The COVID-19 pandemic has highly impacted the communities globally by reprioritizing the means through which various societal sectors operate. Among these sectors, healthcare providers and medical workers have been impacted prominently due to the massive increase in demand for medical services under unprecedented circumstances. Hence, any tool that can help the compliance with social guidelines for COVID-19 spread prevention will have a positive impact on managing and controlling the virus outbreak and reducing the excessive burden on the healthcare system. This perspective article disseminates the perspectives of the authors regarding the use of novel biosensors and intelligent algorithms embodied in wearable IoMT frameworks for tackling this issue. We discuss how with the use of smart IoMT wearables certain biomarkers can be tracked for detection of COVID-19 in exposed individuals. We enumerate several machine learning algorithms which can be used to process a wide range of collected biomarkers for detecting (a) multiple symptoms of SARS-CoV-2 infection and (b) the dynamical likelihood of contracting the virus through interpersonal interaction. Eventually, we enunciate how a systematic use of smart wearable IoMT devices in various social sectors can intelligently help controlling the spread of COVID-19 in communities as they enter the reopening phase. We explain how this framework can benefit individuals and their medical correspondents by introducing Systems for Symptom Decoding (SSD), and how the use of this technology can be generalized on a societal level for the control of spread by introducing Systems for Spread Tracing (SST).

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“…IoMT [12,13] is indeed a set of WiFi smart medical gadgets and smart applications connected to IT health systems through computer networks [14][15][16]. Smart medical devices are equipped with sensors or other computing resources and are exclusively intended for healthcare in the body at home, clinic, hospital, and community [17,18]. ese smart devices are linked to the cloud platforms to analyze collected data for further processing [19].…”

Section: Introductionmentioning

confidence: 99%

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

Bibi

Sikandar

Din

et al. 2020

Journal of Healthcare Engineering

Self Cite

110

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For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

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IoMT-based computational approach for detecting brain tumor

Cited by 62 publications

References 20 publications

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

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