Рақамли технологияларнинг назарий ва амалий масалалари 7-jild 4-son (2024) · 73-79-betlar

Algorithms for Assessment and Segmentation of Fundus Image Quality in Eye Diseases

Mirzayev, N., Shamsiyeva, X.G.

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Annotatsiya

This research paper proposes that integrated approaches to image processing, the use of machine learning algorithms in segmentation, the U-Net method will help to achieve high accuracy in diagnosing various retinal diseases, and an analysis of modern methods is presented for processing and segmentation of fundus images. In this direction, an analysis of scientific and research works carried out using fundus images was carried out. Upon receipt of the results, it was verified that the U-Net method gives effective results in processing and segmentation of fundus images in the DIARETDB1 database.

Fundus kamerako‘z kasalliklarito‘r pardadiabetik retinopatiyaraqamli tasvirtasvir sifatini baholashU-NetsegmentlashFundus cameraeye diseases

Metadata manbasi: jurnal OAI-PMH arxivi · Sindex toʻliq matnni saqlamaydi, manbaga havola beradi.

Iqtibos olish

APA 7
Mirzayev, N. & Shamsiyeva, X.G. (2024). Algorithms for Assessment and Segmentation of Fundus Image Quality in Eye Diseases. Рақамли технологияларнинг назарий ва амалий масалалари, 7(4), 73-79.
GOST R 7.0.5
Mirzayev, N., Shamsiyeva, X.G. Algorithms for Assessment and Segmentation of Fundus Image Quality in Eye Diseases // Рақамли технологияларнинг назарий ва амалий масалалари. 2024. Т. 7. № 4. С. 73-79.
BibTeX
@article{n.2024,
  author  = {Mirzayev, N. and Shamsiyeva, X.G.},
  title   = {Algorithms for Assessment and Segmentation of Fundus Image Quality in Eye Diseases},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year    = {2024},
  volume  = {7},
  number  = {4},
  pages   = {73-79}
}
RIS
TY  - JOUR
AU  - Mirzayev, N.
AU  - Shamsiyeva, X.G.
TI  - Algorithms for Assessment and Segmentation of Fundus Image Quality in Eye Diseases
JO  - Рақамли технологияларнинг назарий ва амалий масалалари
PY  - 2024
VL  - 7
IS  - 4
SP  - 73
EP  - 79
ER  -