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5 November 2019 Heuristic neural network approach in histological sections detection of hydatidiform mole
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Abstract

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.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$28.00 © 2019 SPIE
Patison Palee, Bernadette Sharp, Leonard Noriega, Neil Sebire, and Craig Platt "Heuristic neural network approach in histological sections detection of hydatidiform mole," Journal of Medical Imaging 6(4), 044501 (5 November 2019). https://doi.org/10.1117/1.JMI.6.4.044501
Received: 13 March 2019; Accepted: 7 October 2019; Published: 5 November 2019
JOURNAL ARTICLE
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