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- W2023276550 abstract "No AccessJournal of UrologyClinical Urology: Original Articles1 May 1998BACK PROPAGATION NEURAL NETWORK IN THE DISCRIMINATION OF BENIGN FROM MALIGNANT LOWER URINARY TRACT LESIONS D. PANTAZOPOULOS, P. KARAKITSOS, A. IOKIM-LIOSSI, A. POULIAKIS, E. BOTSOLI-STERGIOU, and C. DIMOPOULOS D. PANTAZOPOULOSD. PANTAZOPOULOS More articles by this author , P. KARAKITSOSP. KARAKITSOS More articles by this author , A. IOKIM-LIOSSIA. IOKIM-LIOSSI More articles by this author , A. POULIAKISA. POULIAKIS More articles by this author , E. BOTSOLI-STERGIOUE. BOTSOLI-STERGIOU More articles by this author , and C. DIMOPOULOSC. DIMOPOULOS More articles by this author View All Author Informationhttps://doi.org/10.1097/00005392-199805000-00057AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract Purpose: We investigated the potential value of morphometry and artificial intelligence tools to discriminate between benign and malignant lower urinary tract lesions. Materials and Methods: The lesions included lithiasis in 50 cases, inflammation in 61, benign prostatic hyperplasia in 99, carcinoma in situ in 5, and grade I and grades II and III transitional cell carcinoma of the bladder in 71 and 184, respectively. Images of routine processed voided urine smears stained by the Giemsa technique were analyzed using a custom image analysis system, providing a data set of 45,452 cells. A neural net model of the back propagation type was used to discriminate benign from malignant cells based on the extracted morphometric and textural features. Data from 13,636 randomly selected cells (30% of the total data) were used as a training set and the data from the remaining 31,816 cells comprised the test set. In a similar attempt to discriminate at the patient level data on 30% of those randomly selected were used to train a back propagation neural net and data on the remaining 329 were used for testing. Results: Application of the back propagation neural net enabled the correct classification of 95.34% of benign and 86.71% of malignant cells with overall 90.57% accuracy. At the patient level the back propagation neural net enabled the correct classification of 100% of those with benign and 94.51% of those with malignant disease with overall 96.96% accuracy. Conclusions: The use of neural nets and image morphometry may increase the speed of cytological diagnosis and the diagnostic accuracy of voided urine cytology. References 1 : Neural Networks: A Comprehensive Foundation.. : Macmillan College Publishing Co.1994. Google Scholar 2 : Nuclear grading of breast carcinoma by image analysis. A. J. C. P.1991; 95: S29. Google Scholar 3 : The application of back propagation neural networks to problems in pathology and laboratory medicine. Arch. Path. Lab. Med.1992; 116: 995. Google Scholar 4 : Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clin. Chem.1992; 38: 34. Google Scholar 5 : Neural networks as an aid in the diagnosis of lymphocyte-rich effusions. Anal. Quant. Cytol. Histol.1995; 17: 48. Google Scholar 6 : Artificial neural networks and their use in quantitative pathology. Anal. Quant. Cytol. Histol.1990; 12: 379. Google Scholar 7 Rosenthal, D. L. and Mango, L. 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Google Scholar From the Department of Urology, University of Athens, Department of Clinical Cytology and Cytogenetics, Laiko Hospital and Department of Informatics, Athens University, Athens, Greece.© 1998 by American Urological Association, Inc.FiguresReferencesRelatedDetailsCited byAbbod M, Catto J, Linkens D and Hamdy F (2018) Application of Artificial Intelligence to the Management of Urological CancerJournal of Urology, VOL. 178, NO. 4, (1150-1156), Online publication date: 1-Oct-2007. Volume 159Issue 5May 1998Page: 1619-1623 Advertisement Copyright & Permissions© 1998 by American Urological Association, Inc.MetricsAuthor Information D. PANTAZOPOULOS More articles by this author P. KARAKITSOS More articles by this author A. IOKIM-LIOSSI More articles by this author A. POULIAKIS More articles by this author E. BOTSOLI-STERGIOU More articles by this author C. DIMOPOULOS More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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