Tracking and Preventing Diseases with Artificial Intelligence (Intelligent Systems Reference Library, 206) PDF
Preview Tracking and Preventing Diseases with Artificial Intelligence (Intelligent Systems Reference Library, 206)
Intelligent Systems Reference Library 206 Mayuri Mehta Philippe Fournier-Viger Maulika Patel Jerry Chun-Wei Lin Editors Tracking and Preventing Diseases with Artificial Intelligence Intelligent Systems Reference Library Volume 206 SeriesEditors JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland LakhmiC.Jain,KESInternational,Shoreham-by-Sea,UK The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and wellstructuredform.Theseriesincludesreferenceworks,handbooks,compendia, textbooks,well-structuredmonographs,dictionaries,andencyclopedias.Itcontains wellintegratedknowledgeandcurrentinformationinthefieldofIntelligentSystems. Theseriescoversthetheory,applications,anddesignmethodsofIntelligentSystems. Virtuallyalldisciplinessuchasengineering,computerscience,avionics,business, e-commerce,environment,healthcare,physicsandlifescienceareincluded.Thelist oftopicsspansalltheareasofmodernintelligentsystemssuchas:Ambientintelli- gence,Computationalintelligence,Socialintelligence,Computationalneuroscience, Artificiallife,Virtualsociety,Cognitivesystems,DNAandimmunity-basedsystems, e-Learningandteaching,Human-centredcomputingandMachineethics,Intelligent control,Intelligentdataanalysis,Knowledge-basedparadigms,Knowledgemanage- ment, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender systems, Robotics and Mechatronics including human-machine teaming, Self-organizing and adap- tive systems, Soft computing including Neural systems, Fuzzy systems, Evolu- tionarycomputingandtheFusionoftheseparadigms,PerceptionandVision,Web intelligenceandMultimedia. IndexedbySCOPUS,DBLP,zbMATH,SCImago. AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience. Moreinformationaboutthisseriesathttp://www.springer.com/series/8578 · · Mayuri Mehta Philippe Fournier-Viger · Maulika Patel Jerry Chun-Wei Lin Editors Tracking and Preventing Diseases with Artificial Intelligence Editors MayuriMehta PhilippeFournier-Viger DepartmentofComputerEngineering SchoolofHumanitiesandSocialSciences SarvajanikCollegeofEngineering HarbinInstituteofTechnology(Shenzhen) andTechnology Shenzhen,Guangdong,China Surat,Gujarat,India JerryChun-WeiLin MaulikaPatel DepartmentofComputerScience, DepartmentofComputerEngineering ElectricalEngineeringand G.H.PatelCollegeofEngineering MathematicalSciences andTechnology WesternNorwayUniversityof CharutarVidyaMandalUniversity AppliedSciences VallabhVidyanagar,Gujarat,India Bergen,Norway ISSN1868-4394 ISSN1868-4408 (electronic) IntelligentSystemsReferenceLibrary ISBN978-3-030-76731-0 ISBN978-3-030-76732-7 (eBook) https://doi.org/10.1007/978-3-030-76732-7 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Allaroundtheworld,thespreadofinfectiousdiseasesisamajorconcernasitdirectly impactsthehealthofpeople.Whilesomeinfectiousdiseasesmayhaveaminorimpact onsociety,somecanhavemajorimpactssuchastherecentSARS-CoV-2coronavirus pandemic,alsoknownasCOVID-19. To cope with the spread of infectious diseases, some traditional approaches are usedsuchastostudytheeffectofmedicinesanddevelopnewones,designappropriate vaccines,andenforcevariousmeasuressuchaswashinghands,wearingfacemasks, and doing temperature checks. However, despite the usage of such measures and medicaladvancements,thereremainseveralincurablediseasesforwhichprevention istheonlycure.Besides,timeisoftencriticalwhencopingwithnewdiseasesthat arehighlycontagioussuchasCOVID-19asnovaccineorveryeffectivemedicineis initiallyavailable. Tocopewiththesechallenges,artificialintelligence(AI)hasbeenrapidlyadopted to assist physicians in diagnosis, disease tracking, prevention, and control. Due to increasingtheavailabilityofelectronichealthcaredataandrapidprogressofanalytics techniques, a lot of research is being carried out in this area by applying machine learninganddataminingtechniquestoassistthemedicalprofessionalsformaking apreliminaryevaluation. Thisbookisacollectionof11chaptersthatprovidesatimelyoverviewofrecent advancesinthisarea,thatis,touseartificialintelligencetechniquesfortrackingand preventing diseases. The target audience of this book is researchers, practitioners, andstudents.Abriefdescriptionofeachchapterisgivenbelow. InChap.1,fourapproachestoidentifystressbyrecognizingtheemotionalstate of a person have been proposed. Pradeep et al. have analyzed the performance of theproposedapproaches usingSurreyAudio-VisualExpressedEmotion(SAVEE) and ENTERFACE databases. The results illustrate the considerable reduction in computationaltimeandshowthatvectorquantization-basedfeaturesperformbetter thanmel-frequencycepstralcoefficientsfeature. In Chap. 2, Fayemiwo et al. compared various approaches for the detection of COVID-19fromX-rayimages.Theproblemisviewedasaclassificationproblem withtwoclasses(normalvsCOVID-19)orthreeclasses(normal,pneumonia,and COVID-19).Afine-tunedVGG-19convolutionalneuralnetworkwithdeeptransfer v vi Preface learningshowsthathighaccuracycanbeobtained(from89%to99%dependingon thescenario. In Chap. 3, Falguni et al. aim to develop an intelligent diagnostic system for glaucoma—an eye-related disease, from the data obtained through clinicians by variousexaminationdevicesorequipmentusedinophthalmology.Theclassification is done by using a hybrid approach using artificial neural network, Naïve Bayes algorithms, decision tree algorithms, and 18 medical examination parameters for a patient. FGLAUC-99 is developed with J48, Naïve Bayes, and MLP classifiers withaccuracyof99.18%.Theaccuracyisnotcomparedwithotherclassifiersasthe datasetisexclusivelydeveloped. InChap.4,Pathaketal.haveintroducedtwoapproaches,onebasedonasimple neural network and another based on a deep convolutional neural network, for diagnosis of tuberculosis disease. To evaluate the performance of the proposed approaches, they conducted experiments using tuberculosis chest X-ray dataset availableonKaggleandreceivedclassificationaccuracyof99.24%. InChap.5,SarumiandLeungproposedanadaptiveNaiveBayes-basedmachine learning algorithm for efficient prediction of genes in the genome of eukaryotic organisms. The adaptive Naive Bayes algorithm provided a sensitivity, specificity, and accuracy of 81.52%, 94.01%, and 96.02%, respectively, on discovering the protein-codinggenesfromthehumangenomechromosomeGRCh37. In Chap. 6, Deshpande et al. presented a survey work on different areas where microscopic imaging of blood cells is used for disease detection. A small note on bloodcompositionisfirstdiscussed,whichisfollowedbyageneralizedmethodology for microscopic blood image analysis for certain application of medical imaging. Several models using microscopic blood cell image analysis are also summarized fordiseasedetection. In Chap. 7, Mahajan and Rana presented a comprehensive review of the recent clinicalnamedentityclassificationusingrule-based,deeplearning-based,andhybrid approaches.Theefficacyofclinicalnamedentityrecognition(NER)techniquesfor informationextractionisalsodiscussedandseveralexperimentsarethenevaluated toshowthestate-of-the-artmodelswithhighaccuracybycombiningdeeplearning (DL)modelswithasequentialmodel. In Chap. 8, the topic of disease diagnosis from CT scan images is discussed. Sajja et al. present a generic and hybrid intelligent architecture for disease diag- nosis. The architecture can classify images into various disease categories using a convolutional neural network and is applied for detecting the COVID-19 disease. Thedesignofthemodelispresentedindetailwithanexperimentalevaluationand adiscussionofapplicationsforotherdiseasediagnosesusingradiologyimages,as wellaspossibilitiesforfuturework. InChap.9,skinlesionclassificationproblemisaddressed.Rocketal.developed an online system to assist doctors to quickly diagnose skin disease through skin lesionobservation.Resultsdemonstrate78%testingaccuracyand84%trainingand validationaccuracy. InChap.10,Ozaetal.havediscussedvariousmammogramclassificationtech- niquesthatarecategorizedbasedonfunction,probability,rule,andsimilarity.They Preface vii havepresentedcomparativeanalysisofthesetechniquesincludingstrengths,draw- backs,andchallenges.Afewmechanismstodealwiththesechallengeshavebeen described.Inaddition,somepubliclyavailablemammogramdatasetsarediscussed inthischapter. InChap.11,Sachdevetal.havepresentedthestate-of-the-artsimilarity-basedand feature-based chemogenomic techniques for the prediction of interaction between drugcompoundsandproteins.Theyhaveillustratedcomparisonofthesetechniques includingtheirmeritsanddemerits. Surat,India MayuriMehta Shenzhen,China PhilippeFournier-Viger VallabhVidyanagar,India MaulikaPatel Bergen,Norway JerryChun-WeiLin March2021 Contents 1 StressIdentificationfromSpeechUsingClusteringTechniques .... 1 PradeepTiwariandA.D.Darji 2 ComparativeStudyandDetectionofCOVID-19andRelated ViralPneumoniaUsingFine-TunedDeepTransferLearning ...... 19 MichaelA.Fayemiwo, ToluwaseA.Olowookere, SamsonA.Arekete, AdewaleO.Ogunde, MbaO.Odim, BosedeO.Oguntunde, OluwabunmiO.Olaniyan, TheresaO.Ojewumi,andIdowuS.Oyetade 3 PredictingGlaucomaDiagnosisUsingAI ........................ 51 FalguniRanadive,AkilZ.Surti,andHemantPatel 4 DiagnosisandAnalysisofTuberculosisDiseaseUsingSimple Neural Network and Deep Learning Approach for Chest X-RayImages ................................................. 77 KetkiC.Pathak, SwathiS.Kundaram, JigneshN.Sarvaiya, andA.D.Darji 5 AdaptiveMachineLearningAlgorithmandAnalyticsofBig GenomicDataforGenePrediction .............................. 103 OluwafemiA.SarumiandCarsonK.Leung 6 MicroscopicAnalysisofBloodCellsforDiseaseDetection: AReview ..................................................... 125 NilkanthMukundDeshpande, ShilpaShaileshGite, andRajanikanthAluvalu 7 InvestigatingClinicalNamedEntityRecognitionApproaches forInformationExtractionfromEMR .......................... 153 PranitaMahajanandDiptiRana ix x Contents 8 Application of Fuzzy Convolutional Neural Network forDiseaseDiagnosis:ACaseofCovid-19DiagnosisThrough CTScannedLungImages ...................................... 177 PritiSrinivasSajja 9 ComputerAidedSkinDisease(CASD)ClassificationUsing MachineLearningTechniquesforiOSPlatform .................. 201 C.AlvinoRock, E.BijolinEdwin, C.Arvinthan, B.KevinJosephPaul,RichardJayaraj,andR.J.S.JebaKumar 10 A Comprehensive Study of Mammogram Classification Techniques ................................................... 217 ParitaOza,YashShah,andMarshaVegda 11 AComparativeDiscussionofSimilarityBasedTechniques andFeatureBasedTechniques forInteractionPrediction ofDrugsandTargets .......................................... 239 KanicaSachdevandManojK.Gupta
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