Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications PDF
Preview Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications
Mourad Elloumi Editor Deep Learning for Biomedical Data Analysis Techniques, Approaches, and Applications Deep Learning for Biomedical Data Analysis Mourad Elloumi Editor Deep Learning for Biomedical Data Analysis Techniques, Approaches, and Applications Editor MouradElloumi ComputingandInformationTechnology TheUniversityofBisha Bisha,SaudiArabia ISBN978-3-030-71675-2 ISBN978-3-030-71676-9 (eBook) https://doi.org/10.1007/978-3-030-71676-9 ©SpringerNatureSwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Contents PartI DeepLearningforBiomedicalDataAnalysis 1-Dimensional Convolution Neural Network Classification TechniqueforGeneExpressionData.......................................... 3 SamsonAnoshBabuParisapogu,ChandraSekharaRaoAnnavarapu, andMouradElloumi ClassificationofSequenceswithDeepArtificialNeuralNetworks: RepresentationandArchitecturalIssues...................................... 27 DomenicoAmato,MattiaAntoninoDiGangi,AntoninoFiannaca, LauraLaPaglia,MassimoLaRosa,GiosuéLoBosco,RiccardoRizzo, andAlfonsoUrso ADeepLearningModelforMicroRNA-TargetBinding .................... 61 AhmetPakerandHasanOg˘ul RecurrentNeuralNetworksArchitecturesforAccidentalFall DetectiononWearableEmbeddedDevices.................................... 81 MirtoMusciandMarcoPiastra PartII Deep Learning for Biomedical ImageAnalysis MedicalImageRetrievalSystemUsingDeepLearningTechniques ....... 101 JiteshPradhan,ArupKumarPal,andHaiderBanka MedicalImageFusionUsingDeepLearning ................................. 129 AshifSheikh,JiteshPradhan,ArpitDhuriya, andArupKumarPal DeepLearningforHistopathologicalImageAnalysis........................ 153 CédricWemmert,JonathanWeber,FriedrichFeuerhake, andGermainForestier v vi Contents Innovative Deep Learning Approach for Biomedical Data InstantiationandVisualization................................................. 171 RyadZemouriandDanielRacoceanu Convolutional Neural Networks in Advanced Biomedical ImagingApplications............................................................ 197 DanielA.Greenfield,GermánGonzález,andConorL.Evans PartIII DeepLearningforMedicalDiagnostics DeepLearningforLungDiseaseDetectionfromChestX-RaysImages... 239 EbenezerJangam,ChandraSekharaRaoAnnavarapu, andMouradElloumi DeepLearninginMulti-OmicsDataIntegrationinCancerDiagnostic ... 255 AbedalrhmanAlkhateeb,AshrafAbouTabl,andLuisRueda UsingDeepLearningwithCanadianPrimaryCareDatafor DiseaseDiagnosis................................................................. 273 Hasan Zafari, Leanne Kosowan, Jason T. Lam, William Peeler, MohammadGasmallah,FarhanaZulkernine,andAlexanderSinger BrainTumorSegmentationandSurveillancewithDeepArtificial NeuralNetworks ................................................................. 311 AsimWaqas,DimahDera,GhulamRasool,NidhalCarlaBouaynaya, andHassanM.Fathallah-Shaykh Index............................................................................... 351 Part I Deep Learning for Biomedical Data Analysis 1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data SamsonAnoshBabuParisapogu,ChandraSekharaRaoAnnavarapu, andMouradElloumi Abstract Inthefieldofbioinformatics,thedevelopmentofcomputationalmethods has drawn significant interest in predicting clinical outcomes of biological data, whichhasalargenumberoffeatures.DNAmicroarraytechnologyisanapproach to monitor the expression levels of sizable genes simultaneously. Microarray gene expression data is more useful for predicting and understanding various diseases such as cancer. Most of the microarray data are believed to be high dimensional, redundant, and noisy. In recent years, deep learning has become a research topic in the field of Machine Learning (ML) that achieves remark- able results in learning high-level latent features within identical samples. This chapter discusses various filter techniques which reduce the high dimensionality of microarray data and different deep learning classification techniques such as 2-DimensionalConvolutionNeuralNetwork (2D- CNN) and 1-Dimensional CNN (1D-CNN).Theproposedmethodusedthefishercriterionand1D-CNNtechniques formicroarraycancersamplesprediction. Keywords Geneexpressiondata · Deeplearning · Convolutionneuralnetwork · Machinelearning · Classification 1 Introduction Computational molecular biology is an interdisciplinary subject that includes dif- ferent fields as biological science, statistics,mathematics, information technology, physics, chemistry and computer science. The analysis of biological data involves thestudyofawiderangeofdatageneratedinbiology.Thisbiologicaldataisgener- S.A.B.Parisapogu·C.S.R.Annavarapu((cid:2)) DepartmentofComputerScienceandEngineering,IndianInstituteofTechnology,Dhanbad, Jharkhand,India e-mail:[email protected];[email protected] M.Elloumi FacultyofComputingandInformationTechnology,TheUniversityofBisha,Bisha,SaudiArabia ©SpringerNatureSwitzerlandAG2021 3 M.Elloumi(ed.),DeepLearningforBiomedicalDataAnalysis, https://doi.org/10.1007/978-3-030-71676-9_1 4 S.A.B.Parisapoguetal. Table1 Geneexpression Sample1 ... Samplej ... SampleM dataformat Gene1 a11 ... a1j ... a1M ... ... ... ... ... ... Genei ai1 ... aij ... aiM ... ... ... ... ... ... GeneN aN1 ... aNj ... aNM atedfromdifferentsources,includinglaboratoryexperiments,medicalrecords,etc. Different types of biological data include nucleotide sequences, gene expression data,macromolecular3Dstructure,metabolicpathways,proteinsequences,protein patterns or motifs and medical images [1]. Unlike a genome, which provides only staticsequenceinformation,microarrayexperimentsproducegeneexpressionpat- ternsthatprovidecellfunctionsdynamicinformation.Understandingthebiological intercellular and intra-cellular processes underlying many diseases is essential for improving the sample classification for diagnostic and prognostic purposes and patienttreatments. Biomedical specialists are attempting to find relationships among genes and disease or formative stages, as well as relationships between genes. For example, an application of microarrays is the revelation of novel biomarkers for cancer, which can give increasingly exact determination and monitoring tools for early recognition of a specific subtype of disease or assessment of the viability of a particular treatment protocol. Different technologies are used to interpret these biological data. For example, microarray technology is useful for measuring the expression levels of a large number of genes under different environmental con- ditions,andNextGenerationSequencing(NGS)Technologyformassivelyparallel DNA sequencing. This kind of experiments on a large amount of biological data leads to an absolute requirement of collection, storage and computational analysis [2]. Inthelastdecade,biologicaldataanalyticshasimprovedwiththedevelopment ofassociatedtechniquessuchasMachineLearning(ML),EvolutionaryAlgorithms (EA) and DeepLearning (DL). These techniques are capable of handling more complexrelationshipsinthebiologicaldata.Forexample,thepredictionofcancer diseasefromthemicroarraydatacanbecarriedoutusingdifferentMLalgorithms (classification and clustering). While dealing with the microarray datasets, which has high dimensionality, are usually complex and noisy makes the classification taskinconvenient[3,4].Table1presentsthegeneexpressiondataformat. Due to this high dimensionality and redundancy, usual classification methods became challenging to apply on gene expression data efficiently. To reduce the problem of high dimensionality, improving learning accuracy and removing irrelevant data from gene expression data, many filter [48] and wrapper [49] approacheswereapplied.Thefiltermethodselectsfeaturesubsetsindependentlyof any learning algorithm and relies on various measures of the general characters of the training data. The wrapper method uses the predictive accuracy of a 1-DimensionalConvolutionNeuralNetworkClassificationTechniqueforGene... 5 predeterminedlearningalgorithmtodeterminethegoodnessoftheselectedsubsets and is computationally expensive. Some of the filter methods are as follows: Z-Score, MinimumRedundancyandMaximumRelevance (mRMR) [5], T-Test [6], InformationGain [7], Fishercriterion [8] and K-Means-Signal-to-NoiseRatio (KM-SNR) ranking [9] etc. The effective gene selection aims to select the small subset of essential genes that are highly predictive and maximizing the ability of classifierstoclassifythesamplesaccurately.Thetaskoffindingreductsisreported tobeNP-hard[10,11]. Artificialintelligence(AI)isthereproductionofhumanintelligencebymachine and computer systems. This evolution of reproduction done by learning—rules to use information, reasoning—rules to reach proper execution and self-correction— take appropriate actions based on learning, reasoning procedures. ML is made use of computational methods to get better machinery performances by detecting influential patterns and inconsistency information. ML makes decisions based on whattheymemorizeorlearnfromdata. DL solves problems that were difficult with ML. DL uses Artificial Neural Networks (ANNs) [33]. An ANN acts very much like a human brain to increase computationalworkandprovideaccurateresults.Atpresent,AIisgettingsmarter by changing the way of its branches, namely ML and DL with enormous com- putationalpowers.ConvolutionalNeuralNetwork(CNN)mimicsbrainfunctionin processinginformation[19]. In this chapter, 1D-CNN, which is a multilayered DL algorithm, is proposed to classify microarray cancer data to recognize the kind of disease. CNN has the capacityinmanagingwithinsufficientdataandboostingclassificationperformance. Furthermore, CNN is also influential in detecting latent characteristics of cancer from comparable types. The organization of the chapter is as follows. Section 2 describestherelatedworksofgeneexpressiondataclassification.Thepreliminaries, which includes various filter and DL classification techniques, are explained in Sect. 3.Section 4provides thedetailsoftheselecteddatasets.Section5describes theproposedapproach,andSect.6presentstheexperimentalresults.Finally,Sect.7 concludesthechapter. 2 RelatedWorks The accessibility of open repositories of data that are appropriate for AI research is highly essential to the field of biomedical informatics. The UniversityofCalifornia,Irvine (UCI) ML repository (https://archive.ics.uci.edu/ ml/index.php),whichcontainsacollectionofdatabases,anddatagenerators,which areusedbytheMLcommunityfortheempiricalanalysisofMLalgorithms.Ithas enabledmanynumbersofanalyststodemonstratetheperformanceofnewstatistical and ML algorithms. ML has excellent accuracy results when it has pre-processed data,butinreal-timeapplications,itisnotsoeasytoobtainpre-processeddata.
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