An Overview on Diagnosis of Endometriosis Disease Based on Machine Learning Methods
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3289-0_20
Reference29 articles.
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2. Agarwal SK, Chapron C, Giudice LC, Laufer MR, Leyland N, Missmer SA, Singh SS, Taylor HS (2019) Clinical diagnosis of endometriosis: a call to action. Am J Obstet Gynecol 220(4):354.e1-354.e12. https://doi.org/10.1016/j.ajog.2018.12.039
3. Chiorean DM, Mitranovici MI, Toru HS, Cotoi TC, Tomuț AN, Turdean SG, Cotoi OS (2023) New insights into genetics of endometriosis—a comprehensive literature review. Diagnostics 13:2265. https://doi.org/10.3390/diagnostics13132265
4. Liu Y, Zhou Q, Peng B, Jiang J, Fang L, Weng W, Wang W, Wang S, Zhu X (2022) Automatic measurement of endometrial thickness from transvaginal ultrasound images. Front Bioeng Biotechnol 10(853845):29. https://doi.org/10.3389/fbioe.2022.853845
5. Javaid M, Haleem A, Singh RP, Suman R, Rab S (2022) Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw 3:58–73. ISSN 2666-6030. https://doi.org/10.1016/j.ijin.2022.05.002
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