Mohsen Farid
| Name | Mohsen Farid |
|---|---|
| Job title | Senior Lecturer in Data Science |
| Research institute | College of Science and Engineering |
Research outputs
Shearlet transform and convolutional neural network for histopathology images in breast cancer classification
Khairi, S. S. M., Bakar, M. A. A., Alias, M. A., Bakar, S. A., Rosli, N. and Farid, M. 2025. Shearlet transform and convolutional neural network for histopathology images in breast cancer classification . The Malaysian Journal of Fundamental and Applied Sciences. 21 (4), p. 2288−2302. https://doi.org/10.11113/mjfas.v21n4.38422288MohdKhairietal.|MalaysianJournalofFundamentalandAppliedSciences,Vol.21(2025)2288−2302RESEARCHARTICLEShearletTransformandConvolutionalNeuralNetworkforHistopathologyImagesinBreastCancerClassificationSitiShalizaMohdKhairia,b,MohdAftarAbuBakarb*,MohdAlmieAliasb,SakhinahAbuBakarb,NurwahyunaRoslic,MohsenFariddaFacultyofComputerandMathematicalSciences,UniversitiTeknologiMARA,40450ShahAlam,Selangor,Malaysia;DepartmentofMathematicalSciences,FacultyofScienceandTechnology,UniversitiKebangsaanMalaysia,43600UKMBangi,Selangor,Malaysia;cDepartmentofPathology,FacultyofMedicine,HospitalCanselorTuankuMuhriz,UniversitiKebangsaanMalaysia,JalanYaacobLatif,BandarTunRazak,Cheras,56000KualaLumpur,Malaysia;dDepartmentofComputingandMathematics,UniversityofDerby,KedlestonRoad,Derby,DE221GB,UnitedKingdomAbstractBreastcancerstandsoutasoneoftheglobalhealththreats,asitmaycausedeathifimproperlytreated.Thus,detectingtheillnessattheearlystagethroughprecisediagnosisisimportanttopreventprogressionoftumorswitheffectivetreatmentsthroughmedicalimaging.Traditionally,manualdiagnosticprocessesrelyontheinputdatarepresentationandexpertknowledge,whichconsumemuchtimeandarepronetohumanerrorduetoheavyworkloadsandfatigue.Recently,deeplearninghasshowndistinguishingresultsinmedicalimaginganalysisforimageclassificationanddetection.Nevertheless,theincreasingdemandtoenhancetheperformanceofimageclassificationisbecomingmoreprominent.Inthisstudy,ahybridmethodofdeeplearningisproposedbycombiningShearlettransformandconvolutionalneuralnetwork(CNN)forbreastcancerhistopathologyimageclassification.First,thehistopathologyimagesaredecomposedusingShearlettransformforShearletcoefficients.Then,theCNNapproachisusedtoclassifytheimagesintobenignandmalignantwithminimalpre-processingprocedure.TheabilityofShearlettransformtoaddresssingularitieshelpstoincreasethequalityofimages.TheproposedhybridmodelimprovestheperformanceoftheoriginalbasicCNNmodel.Resultsfromtheexperimentshowthattheproposedhybridmodelachievesanaccuracyof75%,anF1-scoreof85%formalignanttumor,andamisclassificationrateof0.25%.ThisresultshowsthattheuseofShearlettransformasthefirstfeatureextractionlayerintheCNNarchitectureprovidesbetterfeatureextraction,consequentlyleadingtoimprovedaccuracyforimageclassification.Keywords:Shearlettransform,imageclassification,convolutionalneuralnetwork,breastcancer.IntroductionBreastcancerisoneofthemostcommonillnesseshighlightedbyresearchersinmedicalfieldasitcontributestohighestdeathrates[1].Thiscancerpredominantlyoccursinwomen,hitsthemafterpubertywheretheincidenceratesrisingsteadilyaspeoplegrowolder.Itisanticipatedthatthenumberofdeathsfrombreastcancerwillgrowcontinuouslyfrom2020to2030forEastandSouthAsiancountries[2].Breastcancerisnotanairborneorspreadingillness.Theaffectedbreastcellsexperienceunregulatedgrowthandcandevelopintobenignormalignanttumors.Malignanttumors(canceroustumors)canmetastasizeorspreadtootherpartsofthebody,conferringworseprognosisforthepatients[3].Earlydiagnosisandaccuratetreatmentmayhelpindeceleratingthegrowthprocessandpreventspreadingofuncontrolledabnormalcellstootherpartsofthebody.*Forcorrespondence:aftar@ukm.edu.myReceived:04Sept.2024Accepted:25June2025©CopyrightMohdKhairi.ThisarticleisdistributedunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsunrestricteduseandredistributionprovidedthattheoriginalauthorandsourcearecredited.Storage aware data management system for Genomics
Shah, Z. and Farid, M. 2024. Storage aware data management system for Genomics. 5th International Conference on Big-data Service and Intelligent Computation. ACM Press. https://doi.org/10.1145/3633624Neurotechnological solutions for post-traumatic stress disorder: A perspective review and concept proposal
Laugharne, R., Farid, M., James, C., Dutta, A., Mould, C., Molten, N., Laugharne, J. and Shankar, R. 2023. Neurotechnological solutions for post-traumatic stress disorder: A perspective review and concept proposal. Healthcare Technology Letters. 10 (6), pp. 133-138. https://doi.org/10.1049/htl2.12055Frontal view gait recognition with fusion of depth features from a time of flight camera
Afendi Tengku Mohd, Kurugollu, Fatih, Crookes, Danny, Bouridane, Ahmed and Farid, Mohsen 2018. Frontal view gait recognition with fusion of depth features from a time of flight camera. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2018.2870594Cloud-based video analytics using convolutional neural networks.
Yaseen, M., Anjum, Ashiq, Farid, Mohsen and Antonopoulos, Nick 2018. Cloud-based video analytics using convolutional neural networks. Software Practice and Experience. https://doi.org/10.1002/spe.2636Comparative study of the scaling behavior of the Rényi entropy for He-like atoms
Farid, M, Abdel-Hady, A, Nasser, I and Farid, Mohsen 2017. Comparative study of the scaling behavior of the Rényi entropy for He-like atoms. IOP Publishing. https://doi.org/10.1088/1742-6596/869/1/012011Contextualizing geometric data analysis and related data analytics: A virtual microscope for big data analytics
Farid, Mohsen and Murtagh, Fionn 2017. Contextualizing geometric data analysis and related data analytics: A virtual microscope for big data analytics. Journal of Interdisciplinary Methodologies and Issues in Sciences. https://doi.org/10.18713/JIMIS-010917-3-1Video authentication based on statistical local information
Al-Athamneh, Mohammad, Crookes, Danny and Farid, Mohsen 2016. Video authentication based on statistical local information. IEEE.Digital video source identification based on green-channel photo response non-uniformity (G-PRNU)
Al-Athamneh, Mohammad, Kurugollu, Fatih, Crookes, Danny and Farid, Mohsen 2016. Digital video source identification based on green-channel photo response non-uniformity (G-PRNU). https://doi.org/10.5121/csit.2016.61105The structure of argument: Semantic mapping of US supreme court cases
Murtagh, Fionn and Farid, Mohsen 2015. The structure of argument: Semantic mapping of US supreme court cases. Springer. https://doi.org/10.1007/978-3-319-17091-6_341068
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