Algorithm for Symmetric Additional Two-Dimensional Delineation of Computer X-Ray Images

Turakulov, Sh.X.

Рақамли технологияларнинг назарий ва амалий масалалари · 2023-yil

Annotatsiya

The COVID-19 epidemic spread to all corners of the world, resulting in numerous infections and deaths. This research proposes a symmetric additional two-dimensional classification framework based on three main modules: the preprocessing module for weakly supervised segmentation (O-WSSPM), the asymmetric two-dimensional module (S-CBM), and the Fuzzy C-Means clustering visualization module (FCMM). The first module, O-WSSPM, extracts additional features from CT images to create a new Data1-Seg dataset, primarily focusing on preserving essential feature areas. The second module, S-CBM, utilizes two asymmetric networks to separate various features and obtain additional functionalities. The third module, FCMM, allows the visualization of lesions in non-contrast images. While the data volume is low, five-fold cross-validation is employed to improve diversity. The proposed network shows an average classification accuracy of 85.3%, demonstrating its superior performance when compared to the baseline six-category classification model.

Maqola ma’lumotlari
MualliflarTurakulov, Sh.X.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
Nashr sanasi2023-12-26
Jild6
Son4
Betlar58-66
TilRus
DOI10.62132/ijdt.v6i4.135

Kalit so‘zlar

COVID-19, deep learning, classification, weak supervision, segmentation, additional two-dimensional, FCM, COVID-19, chuqur oʻrganish, tasniflash, zaif nazorat, segmentatsiya, qoʻshimcha ikki chiziqli, FCM

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