3D Convolution Neural Network Based Ensemble Model to Detect Endometrium Issues at Early Stages and Enhance Fertility Chances in Women

Abstract

Endometriosis is a frequent progressive illness in women's health when tissues similar to uterine liner are seen in other sections of the body such as ovaries, fallopian and other reproductive organs. In women, pelvic discomfort and infertility are one of the most prevalent reasons. It is still unknown the real aetiology of endometriosis and very hard to detect. In this research we aim to discover the diagnosis drivers by using ensemble machine learning model from endometriosis. If the chance of endometriosis can be predicted adequately in advance, the main risks of infertility and other health concerns can be eliminated in a large measure. The patients affected can therefore be provided suitable medical attention and treatment. The studies in the article depict that the proposed ensemble model out performs the conventional machine learning algorithms.