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.
| Mualliflar | Turakulov, Sh.X. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2023-12-26 |
| Jild | 6 |
| Son | 4 |
| Betlar | 58-66 |
| Til | Rus |
| DOI | 10.62132/ijdt.v6i4.135 |
DOI: 10.62132/ijdt.v6i4.135 · Maqolaning asl sahifasi
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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