Рақамли технологияларнинг назарий ва амалий масалалари Volume 6 Issue 4 (2023) · pp. 58-66
Algorithm for Symmetric Additional Two-Dimensional Delineation of Computer X-Ray Images
Turakulov, Sh.X.
Abstract
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.
COVID-19deep learningclassificationweak supervisionsegmentationadditional two-dimensionalFCMCOVID-19chuqur oʻrganishtasniflash
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