EAI/Springer Innovations in Communication and Computing Chirag Paunwala · Mita Paunwala · Rahul Kher · Falgun Thakkar · Heena Kher · Mohammed Atiquzzaman · Norliza Mohd. Noor   Editors Biomedical Signal and Image Processing with Artificial Intelligence EAI/Springer Innovations in Communication and Computing SeriesEditor ImrichChlamtac,EuropeanAllianceforInnovation,Ghent,Belgium The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engi- neering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineering include, but arenotlimitedto,wirelessnetworks;mobilecommunication;designandlearning; gaming;interaction;e-healthandpervasivehealthcare;energymanagement;smart grids;internetofthings;cognitiveradionetworks;computation;cloudcomputing; ubiquitousconnectivity,andinmodegeneralsmartliving,smartcities,Internetof Thingsandmore.Theseriespublishesacombinationofexpandedpapersselected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls forcontributionofbooktitlesandindividualchapters,togethermaintainSpringer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representativesinallthosewalksoflifeaffectedadcontributingtotheinformation revolution. Indexing:ThisseriesisindexedinScopus,EiCompendex,andzbMATH. About EAI - EAI is a grassroots member organization initiated through coopera- tion between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellencebycommunity. Chirag Paunwala • Mita Paunwala • Rahul Kher • Falgun Thakkar • Heena Kher • Mohammed Atiquzzaman • Norliza Mohd. Noor Editors Biomedical Signal and Image Processing with Artificial Intelligence Editors ChiragPaunwala MitaPaunwala Electronics&CommunicationEngineering Electronics&CommunicationEngineering SarvajanikCollegeofEngineeringand C.K.PithawalaCollegeofEngineeringand Technology Technology Surat,India Surat,India RahulKher FalgunThakkar Electronics&CommunicationEngineering Electronics&CommunicationEngineering G.H.PatelCollegeofEngineering& G.H.PatelCollegeofEngineering& Technology Technology VallabhVidyanagar,Gujarat,India VallabhVidyanagar,Gujarat,India HeenaKher MohammedAtiquzzaman A.D.PatelInstituteofTechnology SchoolofComputerScience NewVallabhVidyanagar,India UniversityofOklahoma Norman,OK,USA NorlizaMohd.Noor UTMRazakSchool,MenaraRazak UniversitiTeknologiMalaysia KualaLumpur,Malaysia ISSN2522-8595 ISSN2522-8609 (electronic) EAI/SpringerInnovationsinCommunicationandComputing ISBN978-3-031-15815-5 ISBN978-3-031-15816-2 (eBook) https://doi.org/10.1007/978-3-031-15816-2 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerland AG2023 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,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface This book mainly focuses on advanced techniques used for feature extraction, analysis,recognition,andclassificationintheareaofbiomedicalsignalandimage processing.ThisistheeraoftheInternetofThings,cloudcomputing,andartificial intelligence for biomedical signals and images. This book will provide a great platform to the researchers who are working in the area of artificial intelligence forbiomedicalapplications.Biomedicaldataanalysisplaysaveryimportantrolein research as well as in clinical purpose for different types of diagnosis. Moreover, processing a huge amount of data is a challenging task that requires parallel processing.Foradvanced-levelresearch,deeplearning–basedapproacheshavebeen adoptedbyresearcherssincethelastfewyears.Thechaptersinthisbookwillcover allaspectsofartificialintelligence,machinelearning,anddeeplearninginthefield ofbiomedicalsignalandimageprocessingusingnovelandunexploredtechniques andmethodologies. Chapter“VoicePrivacyinBiometrics”summarizesvoiceprivacyinbiometrics. In this chapter, the design of second-order resonator and the linear prediction modelingofspeechproductionisexploitedtodesignvoiceprivacysystem.Theper- formance oftheproposed systemiscompared withthesecondary baseline system oftheINTERSPEECH2020voiceprivacychallenge.Improvedperformance-wise EER and WER are achieved for various subsets of the corpora. Furthermore, anonymizationisachievedbycryptography. Chapter“HistopathologyWhole-SlideImageAnalysisforBreastCancerDetec- tion”listsanovelmethodforweaklysupervisedhistopathologywhole-slideimage (WSI) classification for addressing the breast cancer detection task. Some of the salient aspects of our approach include extracting embeddings from a pre-trained CNN network, using a cosine-loss and training schedule for the classification network, and suggesting an overall decision-making criteria for the WSI based on intermediate decisions on local random selection. We also provide an extensive review of closely related methods with an elaborate compression analysis of the embeddingsusedinthese. Chapter“LungClassificationforCovid-19”presentsacloud-basedlungdisease classificationsystemwheremedicalpractitionercanuploadtheirpatients’chestX- v vi Preface rayontothecloud,andthesystemwillclassifyeitherdiseaseabsent(normal)and disease present (abnormal). For disease present, the system will then classify into lunginfectedwithCovid-19andnon-Covid. Chapter“GRU-BasedParameter-EfficientEpilepticSeizureDetection”presents a gated recurrent unit–based deep learning architecture for accurate epileptic seizure detection has been proposed to reduce the burden on the medical and the paramedical fraternity. The developed model automates the entire process and circumventstherequirementofdeployinganymanualfeature-extractionsteps. Chapter “An Object Aware Hybrid U-Net for Breast Tumour Annotation” describesdigitalexaminationofhistopathologicalslides,andthepathologistanno- tates the slides by marking the rough polygonal boundary around the suspected tumorregion.Thepolygonalboundarycoverstheextentofthetumorintheslide. Chapter“VLSIImplementationofsEMGBasedClassificationforMuscleActiv- ityControl”presentsthereal-timeclassificationofEMG-basedpatternrecognition usinglineardiscriminantanalysis(LDA)andquadraticdiscriminantanalysis. Chapter “Content Based Image Retrieval Techniques and Their Applications in MedicalScience”addressestheCBIRtechniqueswhichareclassifiedintomultiple categoriesbasedonthefeatureextractionandretrievalmechanism.Thesecategories are feature-based, machine learning-based, and deep learning-based methods. The pioneertechniquesforeachcategoryareexplainedindetailinthischapter. Chapter“DataAnalyticsonMedicalImageswithDeepLearningApproach”dis- cussesseveraltechniquesanddecisionpoliciestodynamicallydecidecomputation offloading for smart devices. It adopts a binary offloading policy so that each task ofthesmartdeviceisexecutedonboardorcompletelyoffloadedtotheEdgeServer. Thealgorithmsjointlyoptimizethedensenetworkofdevicesandreducetheoverall latencyandincreasethebatterylifetime. Chapter “Analysis and Classification of Dysarthric Speech” attempts to under- stand how dysarthric speech is different from normal speech through various analyses, such as time-domain representation, linear prediction residual, Teager energyprofile,andtime-frequency-domainrepresentation.Inaddition,thischapter alsoexploresthedeeplearningmethodfortheclassificationofnormalvsdysarthric speech. Chapter “Skin Cancer Detection and Classification Using DWT-GLCM with Probabilistic Neural Networks” presents skin cancer detection and classification usingDWTGLCMwithprobabilisticneuralnetworks.Authorsusedthemaximum efficiency of the system by using, PNN for classification of skin cancer with the gray level co-occurrence matrix(GLCM); discrete wavelet transform (DWT) and statisticalcolorfeatures,respectively. Chapter“ManufacturingofMedicalDevicesUsingArtificialIntelligenceBased Troubleshooter” discusses a process in which an artificial intelligent (AI) agent, independent of human skills, would learn the tricks of trade in exactly the same fashionasahumanwould.ThisworkshowcasesanAIagentthatgainsknowledge of the manufacturing process exactly the same way as an operator learns on the productionfloor. Chapter “Enhanced Hierarchical Prediction for Lossless Medical Image Com- pression in the Field of Telemedicine Application” addresses two algorithms Preface vii of MHPCA for high frequency regions which improves coding efficiency and temporal scalability for Enhanced Hierarchical Prediction for Lossless Medical ImageCompressionintheFieldofTelemedicineApplication. Chapter“LBPBasedCADSystemDesignsforBreastTumorCharacterization” proposesanefficientCADsystemforcharacterizationofbreastultrasoundimages basedonLBPtexturefeaturesandmorphologicalfeatures.Theresultsillustratethat CADsystembasedonANFC-LHalgorithmyieldsoptimalperformanceforbreast tumorcharacterization. Chapter“DetectionofFetalAbnormalityUsingANNTechniques”proposesan approachofneuralmodelingforthediagnosisoffetusabnormalityusingultrasound (US) images. The proposed method is a hybrid approach to image processing methodsandartificialneuralnetworkasaclassifiertoextractfetusabnormality. Chapter“MachineLearningandDeepLearning-BasedFrameworkforDetection and Classification of Diabetic Retinopathy” is a review of examining the prior and recent new algorithms designed to automatically detect and classify diabetic retinopathy. Chapter “Applications of Artificial Intelligence in Medical Images Analysis” discusses how the use of AI has shown promising resultsin thefield of radiology, where the disease can be diagnosed and assessed accurately for efficient decision- makingandplanningofthetreatmentprocedures. Chapter “Intelligent Image Segmentation Methods Using Deep Convolutional Neural Network” presents the underlying general mathematical operations com- bined with the currently used handy performance metrics for Intelligent Image SegmentationMethodsusingDeepConvolutionalNeuralNetwork. Chapter “Artificial Intelligence Assisted Cardiac Signal Analysis for Heart Disease Prediction” discusses a detailed survey of various mathematical and artificialintelligence(AI)-basedcardiacsignalanalysismodelsforcoronarydisease prediction. Chapter“EarlyLungCancerDetectionbyUsingArtificialIntelligenceSystem” is about computer-aided diagnosis (CAD) system used for the prediction of lung cancer, which helps to attain a high detection rate and reduces the time consumed foranalyzingthesample. Chapter “An Optimal Model Selection for COVID 19 Disease Classification” introducesastudyforunderstandingwhichdeeplearningmodelsgivethebestresult whenclassifyingCOVID-19patientsusingchestCTimages. Surat,India ChiragPaunwala Surat,India MitaPaunwala VallabhVidyanagar,Gujarat,India RahulKher VallabhVidyanagar,Gujarat,India FalgunThakkar VallabhVidyanagar,Gujarat,India HeenaKher Norman,OK,USA MohammedAtiquzzaman KualaLumpur,Malaysia NorlizaMohd.Noor Contents VoicePrivacyinBiometrics..................................................... 1 PriyankaGupta,ShrishtiSingh,GauriP.Prajapati,andHemantA.Patil HistopathologyWholeSlideImageAnalysisforBreastCancerDetection 31 PushapDeepSingh,ArnavBhavsar,andK.K.Harinarayanan LungClassificationforCOVID-19............................................. 57 NorlizaMohd.NoorandMuhammadSamerSallam GRU-BasedParameter-EfficientEpilepticSeizureDetection............... 73 OjasA.Ramwala,ChiragN.Paunwala,andMitaC.Paunwala AnObjectAwareHybridU-NetforBreastTumourAnnotation........... 87 SuvidhaTripathiandSatishKumarSingh VLSIImplementationofsEMGBasedClassificationforMuscle ActivityControl .................................................................. 107 AmitM.Joshi,NatashaSingh,andSriTeja Content-Based Image Retrieval Techniques and Their ApplicationsinMedicalScience................................................ 123 MayankR.KapadiaandChiragN.Paunwala DataAnalyticsonMedicalImageswithDeepLearningApproach ........ 153 S.Saravanan,K.Surendheran,andK.Krishnakumar AnalysisandClassificationDysarthricSpeech ............................... 167 SiddhantGuptaandHemantA.Patil SkinCancerDetectionandClassificationUsingDWT-GLCM withProbabilisticNeuralNetworks............................................ 183 J.Pandu,UmadeviKudtala,andB.Prabhakar Manufacturing of Medical Devices Using Artificial Intelligence-BasedTroubleshooters............................................ 195 AkbarDoctor ix x Contents EnhancedHierarchicalPredictionforLosslessMedicalImage CompressionintheFieldofTelemedicineApplication ...................... 207 KetkiC.Pathak,JigneshN.Sarvaiya,andAnandD.Darji LBP-BasedCADSystemDesignsforBreastTumorCharacterization .... 231 Kriti,JitendraVirmani,andRavinderAgarwal DetectionofFetalAbnormalityUsingANNTechniques..................... 259 VidhiRawat,VibhakarShrimali,AlokJain,andAbhishekRawat MachineLearningandDeepLearning-BasedFrameworkfor DetectionandClassificationofDiabeticRetinopathy........................ 271 V.PurnaChandraReddyandKiranKumarGurrala ApplicationsofArtificialIntelligenceinMedicalImagesAnalysis......... 287 PushpanjaliGuptaandPrasanKumarSahoo Intelligent Image Segmentation Methods Using Deep ConvolutionalNeuralNetwork................................................. 309 MekhlaSarkarandPrasanKumarSahoo ArtificialIntelligenceAssistedCardiacSignalAnalysisforHeart DiseasePrediction................................................................ 337 PrasanKumarSahoo,SulagnaMohapatra,andHirenKumarThakkar EarlyLungCancerDetectionbyUsingArtificialIntelligenceSystem..... 373 FatmaTaher AnOptimalModelSelectionforCOVID19DiseaseClassification ........ 399 PramodGaur,VatsalMalaviya,AbhayGupta,GautamBhatia,Bharavi Mishra,RamBilasPachori,andDivyeshSharma Index............................................................................... 417