ULTRATOVUSHLI TIBBIY TASVIRLARNI SEGMENTATSIYA QILISHDA ADAPTIV MASKANI ANIQLASHTIRISH ALGORITMI

Ushbu maqolada bemorlarga tashxis qo'yishning eng samarali usullaridan biri bo'lgan ultratovush tasvirlarida bachadon miomasini segmentatsiya qilish uchun adaptive maskani aniqlashtirish algoritmi taklif etilgan. Ushbu algoritm modelda o'qitish natijasida hosil bo'lgan ehtimollik xaritasi, adaptive chegaralash va morfologik tahlil asosida yaratilgan. Segmentatsiya qilish natijasida aniqlangan noaniq chegralar, nuqtali shovqin kabi muammolarni bartaraf etish uchun chegara noaniqligi va struktura yaxlitligi kabi mezonlardan foydalanilgan. Tajriba natijalari shuni ko'rsatdiki, taklif etilayotgan yondashuv segmentatsiya qilish sifatini sezilarli darajada oshirdi. Eng yaxshi natijalar Dice koffitsenti-0.9112, IoU-0.8325, va accuracy-0.9985 ko'rsatkichlaridir.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-12
DOI
https://doi.org/10.5281/zenodo.22718696
Primary Topic
Engineering and Agricultural Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

ULTRATOVUSHLI TIBBIY TASVIRLARNI SEGMENTATSIYA QILISHDA ADAPTIV MASKANI ANIQLASHTIRISH ALGORITMI

A. Kh. Nishanov, Maxtumquli Jumanazarovich Mamatov
Zenodo (CERN European Organization for Nuclear Research)
Engineering and Agricultural Innovations
article

ULTRATOVUSHLI TIBBIY TASVIRLARNI SEGMENTATSIYA QILISHDA ADAPTIV MASKANI ANIQLASHTIRISH ALGORITMI

A. Kh. Nishanov, Maxtumquli Jumanazarovich Mamatov
article en

Abstract

Ushbu maqolada bemorlarga tashxis qo'yishning eng samarali usullaridan biri bo'lgan ultratovush tasvirlarida bachadon miomasini segmentatsiya qilish uchun adaptive maskani aniqlashtirish algoritmi taklif etilgan. Ushbu algoritm modelda o'qitish natijasida hosil bo'lgan ehtimollik xaritasi, adaptive chegaralash va morfologik tahlil asosida yaratilgan. Segmentatsiya qilish natijasida aniqlangan noaniq chegralar, nuqtali shovqin kabi muammolarni bartaraf etish uchun chegara noaniqligi va struktura yaxlitligi kabi mezonlardan foydalanilgan. Tajriba natijalari shuni ko'rsatdiki, taklif etilayotgan yondashuv segmentatsiya qilish sifatini sezilarli darajada oshirdi. Eng yaxshi natijalar Dice koffitsenti-0.9112, IoU-0.8325, va accuracy-0.9985 ko'rsatkichlaridir.

Zenodo (CERN European Organization for Nuclear Research)
Tashkent University of Information Technology (UZ), Turkmen State Pedagogical Institute named Seidnazar Seydi (TM)
Openalex Percentile: Top 7%
Engineering and Agricultural Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.