Show simple item record

AuthorRahman, Tawsifur
AuthorKhandakar, Amith
AuthorRahman, Ashiqur
AuthorZughaier, Susu M.
AuthorAl Maslamani, Muna
AuthorChowdhury, Moajjem Hossain
AuthorTahir, Anas M.
AuthorHossain, Md Sakib Abrar
AuthorChowdhury, Muhammad E.H.
Available date2024-05-09T07:49:10Z
Publication Date2024-02-17
Publication NameCognitive Computation
Identifierhttp://dx.doi.org/10.1007/s12559-024-10259-3
CitationRahman, T., Khandakar, A., Rahman, A., Zughaier, S. M., Al Maslamani, M., Chowdhury, M. H., ... & Chowdhury, M. E. (2024). TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images. Cognitive Computation, 1-20.
ISSN18669956
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85185132065&origin=inward
URIhttp://hdl.handle.net/10576/54786
AbstractTuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread.
Languageen
PublisherSpringer Nature
SubjectDeep learning
Drug-resistant TB
Image processing
Lung segmentation
Tuberculosis detection
TitleTB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images
TypeArticle
Pagination1-20
ESSN1866-9964
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record