Heuristic neural network approach in histological sections detection of hydatidiform mole.
diagnosis
hydatidiform mole
image analysis
molar pregnancy
multineural network
Journal
Journal of medical imaging (Bellingham, Wash.)
ISSN: 2329-4302
Titre abrégé: J Med Imaging (Bellingham)
Pays: United States
ID NLM: 101643461
Informations de publication
Date de publication:
Oct 2019
Oct 2019
Historique:
received:
13
03
2019
accepted:
07
10
2019
entrez:
14
11
2019
pubmed:
14
11
2019
medline:
14
11
2019
Statut:
ppublish
Résumé
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
Identifiants
pubmed: 31720313
doi: 10.1117/1.JMI.6.4.044501
pii: 19066R
pmc: PMC6830426
doi:
Types de publication
Journal Article
Langues
eng
Pagination
044501Informations de copyright
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).
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