Studies in Systems, Decision and Control 311 Aboul Ella Hassanien · Aditya Khamparia · Deepak Gupta · K. Shankar · Adam Slowik   Editors Cognitive Internet of Medical Things for Smart Healthcare Services and Applications Studies in Systems, Decision and Control Volume 311 Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and withahighquality.Theintentistocoverthetheory,applications,andperspectives on the state of the art and future developments relevant to systems, decision making,control,complexprocessesandrelatedareas, asembeddedinthefieldsof engineering,computerscience,physics,economics,socialandlifesciences,aswell astheparadigmsandmethodologiesbehindthem.Theseriescontainsmonographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular valuetoboththecontributorsandthereadershiparetheshortpublicationtimeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI, SCOPUS, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink. More information about this series at http://www.springer.com/series/13304 Aboul Ella Hassanien Aditya Khamparia (cid:129) (cid:129) Deepak Gupta K. Shankar Adam Slowik (cid:129) (cid:129) Editors Cognitive Internet of Medical Things for Smart Healthcare Services and Applications 123 Editors AboulElla Hassanien AdityaKhamparia Department ofComputer andInformation Schoolof Computer Science Technology andEngineering CairoUniversity LovelyProfessional University Giza, Egypt Phagwara, Punjab, India Deepak Gupta K.Shankar Department ofComputer Science Department ofComputer Applications Engineering Alagappa University MaharajaAgrasen Institute Karaikudi, Tamil Nadu,India of Technology Rohini, Delhi,India AdamSlowik Department ofElectronics andComputer Science KoszalinUniversity ofTechnology Koszalin, Poland ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems,DecisionandControl ISBN978-3-030-55832-1 ISBN978-3-030-55833-8 (eBook) https://doi.org/10.1007/978-3-030-55833-8 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseof illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface This book begins with the basics of Internet of Medical Things (IoMT) and introducesthemethodologies,processes,resultsandchallengesassociatedwiththe same. Internet of Medical Things (IoMT) means the collection of medical devices and applications that connect to healthcare information systems via the Internet. Appearance of IoMT technology provides effective and reliable results to support healthcare services. It reduces the cost to consumers by improving clinical and qualityservicesforpatients.Duetoevolutionfrom2018to2025,developmentsin IoThealthcareapplicationsaretobesurereadytoquickenastheInternetofThings is the key part in the advance change of the healthcare business and different partners are increasing their determinations. The main reason behind the success rate of IoMT is having the capability to help, monitor, inform not only caregivers with usage of wearable devices or telematics but provides healthcare providers actual data to identify issues before they become critical. This book will focus on involvementofIoMT-drivenintelligentcomputingmethods,stateofthearts,novel findings and recent advances in medicine and health care due to new technologies and faster communication between users and devices. This is an exciting and emerging interdisciplinary healthcare-related area in which a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Application-oriented papers are expected to contain cloud IoMT analysis, machine learning, computer vision and deep learning-enabled evaluation of the proposed solutions. This book further illustrates the possible challenges in its applications and suggests ways to overcome them. The topic is wide in nature, and hence, every technique and/or solution cannot be discussed in detail. The primary emphasis of this book is to introduce different IoMT-assisted remote healthcare monitoring applications, challenges and concepts to researchers, students and academicians at large. v vi Preface Objective of the Book ThemainaimofthisbookistoprovideadetailedunderstandingofIoMT-assisted applications with involvement of distinct intelligent computing methods and opti- mized algorithms in the field of computer science. This book will endeavor to endowwithsignificantframeworks,theory,designmethodsandthelatestempirical research findings in the area of IoMT to foster healthcare sector that can be put to good use. Organization of the Book This book is organized into 13 chapters with the following brief description: 1. A Review of Applications, Security and Challenges of Internet of Medical Things TheInternetofMedicalThings(IoMT)playsacrucialroleinenhancingthequality, efficiency and effectiveness of its products in the healthcare field. The chapter discussescross-reviewofallIoMTchosenpaperswithsomelatestresearchmaterial and articles combined. This could help researchers consider previous applications, problems, challenges and threats in the healthcare field. The presented work also includes an overview of the IoMT design and how cloud storage technology sup- ports healthcare applications. 2. IoT Enabled Technology in Secured Healthcare: Applications, Challenges and Future Directions ThischapterintroducesInternetofHealthThingswithwearablehealthcaresystems indetailandshowstheinterassociationofinteractionallowedmedicalgadgetsand theircombinationtobroaderscalenetworksofhealthcaretoenhancethehealthof patients, because of this sensitive behavior of systems related to health. 3. A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications Noise generates maximum critical disturbances as well as touches the medical image quality and ultrasound images in the field of biomedical imaging. This chapter highlights the comparative analysis of image denoising problems with the help of Gaussian, Weiner and mean filter using PSNR and related metrics. 4. Applications and Challenges of Cloud Integrated IoMT This chapter focuses on different aspects including the strengths, weakness, pro- spects and challenges of the IoMT integrated cloud computing. It also describes a frameworkforIoThealthcarenetwork(IoThNet)ispresentedwhichillustrateshow hospitals at access layer can collect user information at data persistent layer. Preface vii A description is also provided on how IoMT can help support different diseases with the help of sensors, for example, glucose, pulse, temperature, blood pressure, heart rate, force, etc. 5. Optimal SVM Based Brain Tumor MRI Image Classification in Cloud Internet of Medical Things Thischapterdiscussesthenewdetectionanddiagnosismodelforbraintumor.The proposedgravitationalsearchalgorithmwithgeneticalgorithm(IGSAGA)modelis appliedforfilteringthefeatures,andoptimalsupportvectormachine(SVM)model is applied for classification processes. The results are validated using a benchmark BRATS dataset, and the experimental outcome indicated the supremacy of the projected model. 6. An Effective Fuzzy Logic Based Clustering Scheme for Edge-Computing Based Internet of Medical Things Systems This chapter presents an effective fuzzy logic-based clustering technique for IoMT applications. The presented FC-IoMT technique selects the cluster heads (CHs)basedonfiveinputparameters,namelyenergy,distance,delay,capacityand queue. The proposed model has undergone extensive validation, and the results ensured the superior results under several measures. 7. Automated Internet of Medical Things (IoMT) Based Healthcare Monitoring System This chapter introduces an IoMT-based automated healthcare system for remote patient that helps physicians and their connections. To predict the disease, the system conducts mechanical training using the CHAID algorithm (chi-square automatic interaction detection performs multi-level splits when computing clas- sification trees) and generated multiple distributions during tree sorting to sort this data.Thisstructureallowsthedoctortointerveneimmediatelytohelppatientswith unusual health problems. 8. Deep Belief Network Based Healthcare Monitoring System in IoMT ThischapterdescribeshowtheInternetofThingscanbeusedformachinelearning inhealthcare.Basedonthedeepbeliefnetwork(DBN)andIoMT,i-NXGeVitacan detect normal and abnormal heart rates and classify various defects. The system achieves the accuracy of 97% for the proposed healthcare monitoring system. 9. An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques In this chapter, various machine learning algorithms have been implemented to predict the heart diseases using IoMT. 88.59% accuracy was obtained by logistic regression using majority voting which is far better than the existing system techniques. viii Preface 10. QoS Optimization in Internet of Medical Things for Sustainable Management This chapter demonstrated QoS optimization in IoMT for sustainable management in wireless sensor networks. The result emphasized that hybrid wireless mesh networks (HWMNs) performed more efficiently when compared with ad hoc on-demand distance vector (AODV) and secured AODV (SAODV) routing protocols. 11. An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model The chapter presented an intelligent IoMT-based breast cancer detection and diagnosis using deep learning model. The proposed model performs a set of pro- cesses,namelypreprocessing,K-meansclustering-basedsegmentation,localbinary pattern (LBP)-based feature extraction and deep neural network (DNN)-based classification. The experimental results ensured the superior performance of the LBP-DNN model with the maximum sensitivity of 71.64%, specificity of 75.87% and accuracy of 70.53%. 12. Internet of Medical Things (IoMT) Enabled Skin Lesion Detection and Classification Using Optimal Segmentation and Restricted Boltzmann Machines The proposed chapter introduces a new IoMT-based skin lesion detection and classificationmodelusingoptimalsegmentationandrestrictedBoltzmannmachines (RBMs),named OS-RBM model. The proposed OS-RBM model involves a series of steps, namely image acquisition, Gaussian filtering (GF)-based preprocessing, segmentation, feature extraction and classification. The experimental outcome ensured the effective classification performance of the OS-RBM model with the maximum sensitivity of 96.43%, specificity of 97.95% and accuracy of 95.68%. 13. An IOT Based Medical Tracking System (IMTS) and Prediction with Probability of Infection The chapter discussed an IoT-based medical appliance called IMTS which works on various integrated handheld medical devices such as pulse sensor, thermal sensor and oximeter followed by a data analytic experiment over the data received from all such devices corresponding to any patient. The data and result show that theproposedsystemisahandytoolatdomesticlevelandcarryingagoodconcept. Giza, Egypt Aboul Ella Hassanien Phagwara, India Aditya Khamparia Rohini, India Deepak Gupta Karaikudi, India K. Shankar Koszalin, Poland Adam Slowik June 2020 Contents A Review of Applications, Security and Challenges of Internet of Medical Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Shashank Kumar, Arjit Kaur Arora, Parth Gupta, and Baljit Singh Saini IoT Enabled Technology in Secured Healthcare: Applications, Challenges and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Sukriti Goyal, Nikhil Sharma, Bharat Bhushan, Achyut Shankar, and Martin Sagayam A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Subrato Bharati, Tanvir Zaman Khan, Prajoy Podder, and Nguyen Quoc Hung Applications and Challenges of Cloud Integrated IoMT . . . . . . . . . . . . 67 Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal, and Pinto Kumar Paul OptimalSVMBasedBrainTumorMRIImageClassificationinCloud Internet of Medical Things. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 S. Chidambaranathan, A. Radhika, Veeraraghavan Vishnu Priya, Surapaneni Krishna Mohan, and M. G. Gireeshan An Effective Fuzzy Logic Based Clustering Scheme for Edge-Computing Based Internet of Medical Things Systems . . . . . . 105 V. Sellam, N. Kannan, and H. Anwer Basha Automated Internet of Medical Things (IoMT) Based Healthcare Monitoring System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Velmurugan Subbiah Parvathy, Sivakumar Pothiraj, and Jenyfal Sampson ix