Biomedical Signal and Image Processing with Artificial Intelligence

423 Pages·2023·15.666 MB·English

by  Chirag Paunwala, Mita Paunwala, Rahul Kher, Falgun Thakkar, Heena Kher, Mohammed Atiquzzaman, Norliza Mohd. Noor

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