Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks

P. Vijayalakshmi, K. Muthumanickam, Gayathri Karthik, S. Sakthivel
In: Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology · 2022 · vol. 43(3) , pp. 2243–2251 · doi:10.3233/jifs-212866 · W4205869480
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This study proposes an entroxon-based intelligent water drop back-propagation neural network model for diagnosing infertility by analyzing MRI image features of adenomyosis and endometriosis.

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Abstract

Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.

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Condition tags

endometriosisadenomyosisinfertility

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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last seen: 2026-06-04T00:00:01.174412+00:00
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