Tuberculosis detection using few shot learning
Abstract Tuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good results for TB detection; however, model’s h...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97803-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849698510016872448 |
|---|---|
| author | Kamran Riasat Akhtar Jamil Shaha Al-Otaibi Sania Zeb Saima Riasat Shamsa Kanwal |
| author_facet | Kamran Riasat Akhtar Jamil Shaha Al-Otaibi Sania Zeb Saima Riasat Shamsa Kanwal |
| author_sort | Kamran Riasat |
| collection | DOAJ |
| description | Abstract Tuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good results for TB detection; however, model’s huge number of parameters, size, and compute requirements making it unsuitable for its practical deployment. Owing to scarce annotated datasets in medical domain augmented datasets are generated which is not a recommended technique in medical domain. This study presents TB-FSNet consisting of Few Shot Learning - Prototypical Network (FSL-PT) with a modified MobileNet-V2 backbone, incorporating a Self-Attention layer. The significant contribution of this study is to effectively train TB-FSNet in FSL-PT paradigm with six different backbones. The dataset utilised for this study consists of Montgomery County, and Shenzhen Chest X-ray Dataset combined. The proposed method attains highest accuracy of 93.6% with mere 2.21M parameters and 8.67 MB size, while maintaining high performance metrics such as precision, specificity, and sensitivity. Moreover, TB-FSNet is designed for seamless integration into embedded devices, making it suitable for deployment on edge devises. The model processes Chest X-ray images in real-time, providing immediate confidence scores for disease detection. This capability ensures that users can receive accurate diagnostic insights without needing to wait for medical professionals, enhancing the accessibility and efficiency of TB detection. |
| format | Article |
| id | doaj-art-01fdc1cd62a444e6a8172cc48e0d1b47 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-01fdc1cd62a444e6a8172cc48e0d1b472025-08-20T03:18:53ZengNature PortfolioScientific Reports2045-23222025-04-0115111810.1038/s41598-025-97803-9Tuberculosis detection using few shot learningKamran Riasat0Akhtar Jamil1Shaha Al-Otaibi2Sania Zeb3Saima Riasat4Shamsa Kanwal5Directorate of ICT, Allama Iqbal Open UniversityDepartment of AI & DS, FAST-NUCESDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of AI & DS, FAST-NUCESDepartment of Mathematical Sciences, Fatima Jinnah Women UniversityDepartment of Mathematical Sciences, Fatima Jinnah Women UniversityAbstract Tuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good results for TB detection; however, model’s huge number of parameters, size, and compute requirements making it unsuitable for its practical deployment. Owing to scarce annotated datasets in medical domain augmented datasets are generated which is not a recommended technique in medical domain. This study presents TB-FSNet consisting of Few Shot Learning - Prototypical Network (FSL-PT) with a modified MobileNet-V2 backbone, incorporating a Self-Attention layer. The significant contribution of this study is to effectively train TB-FSNet in FSL-PT paradigm with six different backbones. The dataset utilised for this study consists of Montgomery County, and Shenzhen Chest X-ray Dataset combined. The proposed method attains highest accuracy of 93.6% with mere 2.21M parameters and 8.67 MB size, while maintaining high performance metrics such as precision, specificity, and sensitivity. Moreover, TB-FSNet is designed for seamless integration into embedded devices, making it suitable for deployment on edge devises. The model processes Chest X-ray images in real-time, providing immediate confidence scores for disease detection. This capability ensures that users can receive accurate diagnostic insights without needing to wait for medical professionals, enhancing the accessibility and efficiency of TB detection.https://doi.org/10.1038/s41598-025-97803-9Few Shot LearningTuberculosisSelf Attention |
| spellingShingle | Kamran Riasat Akhtar Jamil Shaha Al-Otaibi Sania Zeb Saima Riasat Shamsa Kanwal Tuberculosis detection using few shot learning Scientific Reports Few Shot Learning Tuberculosis Self Attention |
| title | Tuberculosis detection using few shot learning |
| title_full | Tuberculosis detection using few shot learning |
| title_fullStr | Tuberculosis detection using few shot learning |
| title_full_unstemmed | Tuberculosis detection using few shot learning |
| title_short | Tuberculosis detection using few shot learning |
| title_sort | tuberculosis detection using few shot learning |
| topic | Few Shot Learning Tuberculosis Self Attention |
| url | https://doi.org/10.1038/s41598-025-97803-9 |
| work_keys_str_mv | AT kamranriasat tuberculosisdetectionusingfewshotlearning AT akhtarjamil tuberculosisdetectionusingfewshotlearning AT shahaalotaibi tuberculosisdetectionusingfewshotlearning AT saniazeb tuberculosisdetectionusingfewshotlearning AT saimariasat tuberculosisdetectionusingfewshotlearning AT shamsakanwal tuberculosisdetectionusingfewshotlearning |