An Overview of Machine Learning Techniques Focusing on the Diagnosis of Endometriosis

Endometriosis 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. Images from these procedures are used as input for assorted machine learning techniques inclusive of classification, forecasting, clustering, regression, reinforcement learning, and neural networks. This survey presents the merits and demerits of the aforementioned machine learning methods. Access this chapter Tax calculation will be finalised at checkout Purchases are for personal use only Similar content being viewed by others

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Cell Rep Med 2(9):100400 Author information Authors and Affiliations Corresponding author Editor information Editors and Affiliations Rights and permissions Copyright information © 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. About this paper Cite this paper Naqvi, 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 Download citation DOI: https://doi.org/10.1007/978-981-99-0189-0_6 Published: Publisher Name: Springer, Singapore Print ISBN: 978-981-99-0188-3 Online ISBN: 978-981-99-0189-0 eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science