Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications

358 Pages·2021·8.589 MB·English

by  Mourad Elloumi

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
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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:[e-mail];[e-mail]
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.