Articles

ENDOSKOPIK TASVIRLARDA OSHQOZON YARALARINI ANIQLASH UCHUN SUN’IY INTELLEKT TEXNOLOGIYALARINING QO‘LLANILISHI

Vol. 4 No. 47 (2026): Young Scientists 66-70

DOI: 10.5281/zenodo.20269835 2026-05-18 Articles CC BY 4.0 Open Access

Authors

  • Jalgasbayeva Aziza Artiqbaevna Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti magistranti, Toshkent, O‘zbekiston
  • Sabitova Nazokat Qobuljon qizi Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti Kompyuter tizimlari kafedrasi asistenti, Toshkent, O‘zbekiston

Abstract

Ushbu tadqiqotda endoskopik tasvirlarda oshqozon yaralarini RetinaNet modeli yordamida avtomatik aniqlash va klassifikatsiya qilish masalasi yoritilgan. Tadqiqot davomida Kvasir, HyperKvasir, GastroVision va Kvasir-Capsule ma’lumotlar bazalaridan foydalanilib, maxsus o‘quv tanlanma shakllantirildi. Tasvirlarga dastlabki ishlov berish bosqichida CLAHE, Median Filter va Telea Inpainting algoritmlari qo‘llanildi hamda augmentatsiya usullari orqali o‘quv tanlanma kengaytirildi. RetinaNet modeli endoskopik tasvirlar asosida o‘qitilib, uning ishlash samaradorligi Precision, Recall va mAP ko‘rsatkichlari asosida baholandi. Tajriba natijalari modelning oshqozon yaralarini aniqlashda yuqori samaradorlikka ega ekanligini ko‘rsatdi.

Keywords:

endoskopik tasvirlar oshqozon yarasi sun'iy intellekt kompyuter ko‘rish RetinaNet dastlabki ishlov berish augmentatsiya Focal Loss Feature Pyramid Network.

References

World Health Organization. GLOBOCAN 2022. Stomach cancer fact sheet. https://gco.iarc.who.int

Hernanz N., et al. (2019). Characteristics and consequences of missed gastric cancer. Digestive and Liver Disease.

Singh S.P., Yadla M., et al. (2023). Vision Transformer based gastrointestinal disease classification. Scientific Reports, 13, 11562.

Sobhaninia Z., Abharian N., et al. (2021). Hybrid deep learning model for gastrointestinal disease classification. Biomedical Signal Processing and Control, 68, 102742.

Lin T.Y., Goyal P., Girshick R., He K., Dollar P. (2017). Focal Loss for Dense Object Detection. ICCV, 2980-2988.

He K., Zhang X., Ren S., Sun J. (2016). Deep Residual Learning for Image Recognition. CVPR, 770-778.

Pogorelov K., et al. (2017). Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. MMSys, 164-169.

Jha D., et al. (2020). Kvasir-SEG: A segmented polyp dataset. MMM, 451-462.

Gonzalez R.C., Woods R.E. (2018). Digital Image Processing. Pearson Education.

Qiu Z., et al. (2019). Automatic diagnosis of gastric ulcer using deep convolutional neural networks. IEEE Access, 7, 138632-138641.

Zhang X., Zhao S., et al. (2022). Deep learning-based ulcer detection in wireless capsule endoscopy. Computers in Biology and Medicine, 141, 105134.

Litjens G., et al. (2017). A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60-88.

Downloads

Published

2026-05-18

Issue

Vol. 4 No. 47 (2026): Young Scientists

Section

Articles

License

Copyright (c) 2026 Innovative Academy RSC

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jalgasbayeva, A., & Sabitova, N. (2026). ENDOSKOPIK TASVIRLARDA OSHQOZON YARALARINI ANIQLASH UCHUN SUN’IY INTELLEKT TEXNOLOGIYALARINING QO‘LLANILISHI. Young Scientists, 4(47), 66-70. https://doi.org/10.5281/zenodo.20269835
Innovative Academy RSC
Article metrics Views and PDF downloads
0 Views
0 Downloads