Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray

The proposed methods acquired the Accuracy, Recall, Precision, F-score, and Specificity of 99.0576%, 98.9459%, 99.145%, 98.96%, and 99.152% respectively.

Authors

Institutions

Publication Details

Journal
Journal of X-Ray Science and Technology
Published
2025-01-01
DOI
https://doi.org/10.1177/08953996241300018
Citations
8
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
10.88
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray

P. Visu, P. Ajitha, V Sathiya, R Surendran
8 citations
Journal of X-Ray Science and Technology
COVID-19 diagnosis using AI
10.88
article

Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray

P. Visu, P. Ajitha, V Sathiya, R Surendran
article en
8 citations

Abstract

The proposed methods acquired the Accuracy, Recall, Precision, F-score, and Specificity of 99.0576%, 98.9459%, 99.145%, 98.96%, and 99.152% respectively.

Journal of X-Ray Science and TechnologyVol. 33(1)
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Sathyabama Institute of Science and Technology (IN), Saveetha University (IN)
Good health and well-being
Openalex Percentile: Top 2%
COVID-19 diagnosis using AI
10.88
DOISourceOpenAlex
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.