{"paper_id":"3b0f8395-aba5-49d4-8442-7fca639569e2","body_text":"Abstract\nEndometriosis is a heterogenous, medical problem affecting women possibly leading to a severe decline in quality of life. It occurs due to endometrium-like tissue growing outside of the uterus. Although the endometrial lesions are benign, they cause pain and health concerns and may develop into malignant tumors, inciting endometrial cancer. Several medical imaging methods are employed to diagnose endometriosis including transvaginal ultrasound (TVUS), magnetic resonance imaging (MRI), hysteroscopy, and laparoscopy. We offer a concise analysis of latest advances and the challenges related to the same when machine learning is applied for diagnosis of endometriosis. The methods of image extraction as well as image processing have been compared. Pros and cons of different kinds of databases have been highlighted. 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Cell Rep Med 2(9):100400\nAuthor information\nAuthors and Affiliations\nCorresponding author\nEditor information\nEditors and Affiliations\nRights and permissions\nCopyright information\n© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.\nAbout this paper\nCite this paper\nNaqvi, N.Z., Kaur, K., Khanna, S., Singh, S. (2023). An Overview of Machine Learning Techniques Focusing on the Diagnosis of Endometriosis. In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, vol 1007. Springer, Singapore. https://doi.org/10.1007/978-981-99-0189-0_6\nDownload citation\nDOI: https://doi.org/10.1007/978-981-99-0189-0_6\nPublished:\nPublisher Name: Springer, Singapore\nPrint ISBN: 978-981-99-0188-3\nOnline ISBN: 978-981-99-0189-0\neBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science","source_license":"CC0","license_restricted":false}