An Analogy of Endometriosis Recognition Using Machine Learning Techniques

S Visalaxi, Dinah Punnoose, T. Sudalai Muthu
In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · 2021 · pp. 739–746 · doi:10.1109/icicv50876.2021.9388403 · W3143209498
article OA: closed CC0 ⤵ 9 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-12 ⓘ

This paper surveys machine learning techniques, including logistic regression, CNNs, ANNs, and SVMs, for identifying endometriosis from medical images, analyzing the pros and cons of each method.

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Abstract

Endometriosis is a gynecological disorder which occurs in the age group of 15-45 years. In normal cases endometrial tissue is found inside the lining of uterus but in case f endometrium, tissue is found outside the uterus. In advanced cases these tissues may line other organs such as Kidneys, Pancreas, Liver and Intestines which is called as Deep endometriosis. Endometriosis is both symptomatic and asymptomatic. The symptoms of endometriosis is similar to other symptoms such as pelvic pain, inflammatory disease etc. Unfortunately it can be diagnosed only through laparoscopic procedures. The Laparoscopic procedures causes unrepairable wound, health challenges to the women. This paper surveys various methodologies of identifying the presence of endometriosis through the medical images using machine learning techniques includes “ Logistic Regression, Convolutional Neural network, Artificial Neural Network, Neural Network, Support Vector Machines(SVM), Naive Bayes, Decision Tree” etc.. This survey illustrated the pros and cons of the various methods.

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endometriosis

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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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