CHEST RADIOLOGY / LETTER TO THE EDITOR
Comments on data balance, test-set accounting, and thresholded reporting in an adaptive convolution neural network model for tuberculosis detection and diagnosis using semantic segmentation
1
Department of Biology, Ecology and Earth Science, University of Calabria, Rende (CS), Italy
Submission date: 2025-09-27
Acceptance date: 2025-09-29
Publication date: 2026-01-30
Corresponding author
Pol J Radiol, 2026; 91(1): 45
TOPICS
REFERENCES (3)
1.
Salkade SAS, Rathi SV. An adaptive convolution neural network model for tuberculosis detection and diagnosis using semantic segmentation. Pol J Radiol 2025; 90: e124-e137. DOI: 10.5114/pjr/200628.
2.
Saito T, Rehmsmeier M. The precision–recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 2015; 10: e0118432. DOI: 10.1371/journal.pone. 0118432.
3.
World Health Organization. Use of computer-aided detection software for tuberculosis screening and triage: policy update. Geneva: World Health Organization; 2025.
| ISSN: | 1899-0967 |
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