title: Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images.
abstract: A multi-classifier diagnostic system was designed for distinguishing between benign and malignant thyroid nodules from routinely taken (FNA, H&E-stained) cytological images. To construct the multi-classifier system, several combination rules and different mixtures of ensemble classifier members, employing morphological and textural nuclear features, were comparatively evaluated. Experimental results illustrated that the classifier combination k-NN/PNN/Bayesian and the majority vote rule enhanced significantly classification accuracy (95.7%) as compared to best single classifier (PNN: 89.6%). The proposed system was designed with purpose to be utilized in daily clinical practice as a second opinion tool to support cytopathologists' decisions, when a definite diagnosis is difficult to be obtained.
authors.affiliation: School of Medicine, University of Patras, Rio, Patras 265 04, Greece. daskalakis@med.upatras.gr
authors.forename: Antonis
authors.initials: A
authors.lastname: Daskalakis
authors.forename: Spiros
authors.initials: S
authors.lastname: Kostopoulos
authors.forename: Panagiota
authors.initials: P
authors.lastname: Spyridonos
authors.forename: Dimitris
authors.initials: D
authors.lastname: Glotsos
authors.forename: Panagiota
authors.initials: P
authors.lastname: Ravazoula
authors.forename: Maria
authors.initials: M
authors.lastname: Kardari
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kalatzis
authors.forename: Dionisis
authors.initials: D
authors.lastname: Cavouras
authors.forename: George
authors.initials: G
authors.lastname: Nikiforidis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D044963:Biopsy, Fine-Needle; D002467:Cell Nucleus / Q000378:metabolism; D003581:Cytodiagnosis / Q000379:methods; D003937:Diagnosis, Differential; D004801:Eosine Yellowish-(YS) / Q000737:chemistry; D006416:Hematoxylin / Q000737:chemistry; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D013194:Staining and Labeling / Q000379:methods; D018709:Statistics, Nonparametric; D013961:Thyroid Gland / Q000737:chemistry / Q000473:pathology*; D013964:Thyroid Neoplasms / Q000378:metabolism / Q000473:pathology*; D016606:Thyroid Nodule / Q000175:diagnosis* / Q000378:metabolism
chemical_list: D004801:Eosine Yellowish-(YS); D006416:Hematoxylin