U-NET ARXITEKTURASI VA KONVOLYUTSIYA TARMOG‘IGA ASOSLANGAN BACHADON TASVIRLARINI SEGMENTATSIYALASH ORQALI MIOMANI ANIQLASH JARAYONI

##plugins.themes.bootstrap3.article.sidebar##

PDF (Русский)
Nashr: Jild 2 Nomeri 13 : Eurasian Journal of Academic Research
Bo'lim: Статьи
##semicolon##
##submission.license.cc.by4.footer##

##plugins.themes.bootstrap3.article.main##

Belarus-O‘zbekiston qo‘shma tarmoqlararo amaliy texnik kvalifikatsiyalar instituti o'quv ishlari bo‘yicha prorektori, f.-m.f.d., professor
info@in-academy.uz
Muxammad al – Xorazmiy nomidagi Toshkent Axborot Texnologiyalari universiteti Assistenti
info@in-academy.uz
Muxammad al – Xorazmiy nomidagi Toshkent Axborot Texnologiyalari universiteti magistranti
info@in-academy.uz

Abstrak:

Ushbu maqolada U-NET arxitekturasi va konvolyutsiya tarmog‘iga asoslangan bachadon tasvirlarini segmentatsiyalash orqali miomani aniqlash jarayoni ko‘rib chiqilgan. Mashinali o‘qitishning chuqur konvolyutsiya tarmoqlari keng imkoniyatlarni bermoqda. Ushbu texnologiyalar tibbiy tasvirlarni tahlil qilish uchun avtomatlashtirilgan tizimlarni yaratishga imkon bermoqda. Kasallikdan zararlangan inson talani a’zolarini tahlil qilish uchun mashinali o‘qitishning chuqur konvolyutsiya tarmoqlari keng qo‘llanilmoqda.

##plugins.themes.bootstrap3.article.details##

##submission.howToCite##:

Sadullaeva , S. ., Aripova , Z. ., & Rajabova , M. . (2022). U-NET ARXITEKTURASI VA KONVOLYUTSIYA TARMOG‘IGA ASOSLANGAN BACHADON TASVIRLARINI SEGMENTATSIYALASH ORQALI MIOMANI ANIQLASH JARAYONI. Yevrosiyo Ilmiy Tadqiqotlar Jurnali, 2(13), 1429–1435. Retrieved from https://in-academy.uz/index.php/ejar/article/view/8043

##submission.citations##:

Sadullaeva Sh.A., Aripova Z.D, Rajabova M.R., “Ayollarda uchraydigan mioma kasalligini segmentatsiyalash orqali aniqlash” “Zamonaviy Axborot, Kommunikatsiya Texnologiyalari Va At-Ta’lim Tatbiqi Muammolari” Mavzusidagi Respublika Ilmiy-Amaliy Anjumani, Samarqand, pp. 88–90, 2022.

Садуллаева Шахло Азимбаевна, Арипова Зульфия Дилшодовна (Ташкент, Узбекистан) Распознавание миомы матки с помощью методов распознавание объектов и пятен 135ст. актуальные научные исследования в современном мире выпуск 11(79) Часть 10 Ноябрь 2021 г. ЖУРНАЛ ISSN 2524-0986.

Kuchkorov T.A., Ochilov T.D. Tasvirlarni segmentlashning chuqur o‘qitishga asoslangan arxitektura va algoritmlari. 2021 y.

Weng, Y., Zhou, T., Li, Y., and Qiu, X. (2019). NAS-UNet: neural architecture search for medical image segmentation. IEEE Access 7, 44247–44257. doi: 10.1109/ACCESS.2019.2908991

Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017b). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.

Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) (Munich), 801–818. doi: 10.1007/978-3-030-01234-2_49

Shelhamer, E.; Long, J.; Darrell, T. Fully Convolutional Networks for Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 640–651.

Dai, J.; Li, Y.; He, K.; Sun, J. R-fcn: Object detection via region-based fully convolutional networks. Neural Inf. Process. Syst. 2016, 29, 379–387.

C. Wachinger, M. Reuter, and T. Klein, “DeepNAT: Deep convolutional neural network for segmenting neuroanatomy,” NeuroImage, vol. 170, pp. 434–445, apr 2018.

S. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami et al., “Medical image analysis using convolutional neural networks: A review,” Journal of Medical Systems, vol. 42, no. 11, pp. 226, 2018

Dilshodovna, Aripova Zulfiya. "ALGORITHMS FOR PROCESSING MEDICAL GRAPHIC IMAGES FOR UTREIN FIBROID SEGMENTATION." Web of Scientist: International Scientific Research Journal 3.4 (2022): 1060-1069.