Label Propagation Algorithm for the Slices Detection of a Ground-Glass Opacity Nodule

Aradiologistmustreadhundredsofslicestorecognizeamalignantorbenignlungtumorincomputed tomography(CT)volumedata.Toreducetheburdenoftheradiologist,someproposalshavebeen appliedwiththeground-glassopacity(GGO)nodules.However,theGGOnodulesneedbedetectedand labeledbyaradiologistmanually.SomesliceswiththeGGOnodulecanbemissedbecausethereare manyslicesinseveralvolumedata.Althoughsomepapershaveproposedasemi-supervisedlearning methodtofindthesliceswithGGOnodules,thewasnodiscussionontheimpactofparametersin theproposedsemi-supervisedlearning.Thisarticlealsoexplainsandanalyzesthelabelpropagation algorithmwhichisoneofthesemi-supervisedlearningmethodstodetecttheslicesincludingthe GGOnodulesbasedontheparameters.Experimentalresultsshowthattheproposalcandetectthe slicesincludingtheGGOnoduleseffectively. KeywORDS Computed Tomography (CT) Volume Data, Detection of the Slices Including the Ground-Glass Opacity Nodule, Ground-Glass Opacity Nodules (GGO), Label Propagation Algorithm, Lung Nodules, Semi-Supervised Learning

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