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dc.contributor.authorPatison Paleeen_US
dc.contributor.authorBernadette Sharpen_US
dc.contributor.authorLeonard Noriegaen_US
dc.contributor.authorNeil Sebireen_US
dc.contributor.authorCraig Platten_US
dc.description.abstract© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k-fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15- and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets.en_US
dc.titleHeuristic neural network approach in histological sections detection of hydatidiform moleen_US
article.title.sourcetitleJournal of Medical Imagingen_US
article.volume6en_US Furtwangenen_US Foundation Trusten_US Universityen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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