TIBBIY DIAGNOSTIKADA SUN’IY INTELLEKT: NEYRON TARMOQLAR YORDAMIDA ANIQLIKNI OSHIRISH USULLARI
Keywords:
Sun’iy intellekt, neyron tarmoqlar, kompyuter injiniringi, chuqur o‘rganish (Deep Learning), konvolyutsion neyron tarmoqlari (CNN), tibbiy tasvirlar segmentatsiyasi, U-Net arxitekturasi, ma’lumotlar augmentatsiyasi, raqamli diagnostika, algoritmlar optimallashuvi.Abstract
Ushbu maqolada kompyuter injiniringining zamonaviy yutuqlari, xususan, sun’iy intellekt va neyron tarmoqlarining tibbiy diagnostika jarayonidagi o‘rni tadqiq etiladi. Maqolaning maqsadi kasalliklarni erta aniqlashda algoritmlarning aniqlik darajasini tahlil qilish va inson omili bilan bog‘liq xatoliklarni kamaytirish yo‘llarini ko'rsatib berishdir. Tadqiqot davomida chuqur o'rganish (Deep Learning) modellarining rentgen va MRT tasvirlarini tahlil qilishdagi samaradorligi yoritilgan.
References
1. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, Cham: Springer International Publishing, 2015, pp. 234–241.
2. F. Milletari, N. Navab, and S. A. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 2016, pp. 565–571. doi: 10.1109/3DV.2016.79.
3. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
4. Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet: A Nested U-Net Architecture for Medical Image Segmentation,”in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Cham: Springer, 2018, pp. 3–11.
5. A. Paszke et al., “ PyTorch: An Imperative Style, High-Performance Deep Learning Library, ” in Advances in Neural Information Processing Systems 32, 2019, pp. 8024–8035.