logo
calendar30 Dekabr 2025
view10
Asosiy til:O'zbek

Vanilla U-Net chuqur o‘rganish modeli asosida miya o‘simtalarini tibbiy tasvirlarda segmentatsiya qilishning samarali yondashuvlari

Fan yo'nalishi:Sun'iy intellektHisoblash nazariyasi va matematikaKompyuter grafikasi va kompyuter yordamida dizaynKompyuter tarmoqlari va kommunikatsiyalariAxborot tizimlariSignalni qayta ishlashDasturiy ta'minot
pdf

30._Meliyeva_M.O._-_VAN....pdf

PDF

“Raqamli iqtisodiyot” ilmiy-elektron jurnali

“Raqamli iqtisodiyot” ilmiy-elektron jurnali

Maqola chop etishsee more

MAQOLA ANNOTATSIYASI

quote
Ushbu tadqiqotda tibbiy tasvirlarni, xususan, miya o‘simtalarini avtomatik aniqlash va segmentatsiyalash uchun klassik Vanilla U-Net chuqur o‘rganish modeli qo‘llanildi. Model MRT tasvirlari asosida o‘qitildi va sinovdan o‘tkazildi. U-Net arxitekturasi kodlovchi va dekodlovchi qismlardan iborat bo‘lib, tasvirlarning fazoviy xususiyatlarini saqlagan holda segmentatsiyani amalga oshiradi. Tajribalarda 256x256x3 o‘lchamdagi tasvirlar ishlatildi, natijada DICE koeffitsienti 81% va IoU 68.19% ga yetdi. Post-processing bosqichida segmentatsiya natijalari asl MRT tasvirlari bilan solishtirildi va model samaradorligi tasdiqlandi.

MUALLIFLAR

M.Meliyeva

"SHAROF RASHIDOV NOMIDAGI SAMARQAND DAVLAT UNIVERSITETI" DAVLAT MUASSASASI

Teglar

# CNN# U-Net# enkoder# dekoder# ReLU# DICE koeffitsienti# IoU# post-processing# segmentatsiya# miya o‘simtasi# MRT

O'XSHASH MAQOLALAR

SHU JURNALDAGI BOSHQA MAQOLALAR

Maqolani baholang

0
0 ta baho
5
4
3
2
1

Maqola idintifikatorlari

ROI:

https://eroi.uz/11.142/A026-361644

DOI:

Mavjud emas

Foydalanilgan adabiyotlar

O.Ronneberger, P.Fischer, T.Brox. U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), 201. (pp. 234–241). http://lmb.informatik.uni-freiburg.de/

Xue-Cheng Tai, Hao Liu, Raymond H. Chan, Lingfeng Li. A Mathematical Explanation of UNet, 2024 - arXiv.2410.04434v1 [cs.CV] https://doi.org/10.3934/mfc.2024040

Jatender Kumar, Naveen Dalal, Monika Sethi. Hyperparameters in Deep Learning: A Comprehensive Review. IJISAE, 2024. https://ijisae.org/index.php/IJISAE/article/download/6967/5881/12201

A.A.Taha, A.Hanbury. Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 2015, 15:29. https://doi.org/10.1186/s12880-015-0068-x

G. Luo, P. An, Z. Ji, Y. Chen, M. Li. Post-processing for medical image segmentation: A review. Frontiers in Biomedical Technologies, 9(3), 15–28, 2022. https://doi.org/10.3389/fbioe.2022.869542

Michael Oluwaseun Ayansiji, Friday Zinzendoff Okwonu. Optimizing Deep Learning Hyperparameters Using InterpolationBased Optimization. Control and Optimization in Applied Mathematics - COAM. July 1, 2025.

https://medium.com/@alejandro.itoaramendia/decoding-the-u-net-a-complete-guide-810b1c6d56d8

https://www.mygreatlearning.com/blog/u-net-architecture/

Juan.R.Terven, Diana M. Cordova-Esparza, Alfonzo Ramirez-Pedraza, Edgar A. Chavez-Urbiola. “Loss Functions and Metrics in Deep Learning: A Review.” Under review in computer Science Review. arXiv:2307.02694v1 [cs.LG]. 5 Jul 2023. https://doi.org/10.48550/arXiv.2307.02694