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AuthorRahman, Tawsifur
AuthorKhandakar, Amith
AuthorKadir, Muhammad Abdul
AuthorIslam, Khandaker Rejaul
AuthorIslam, Khandakar F.
AuthorMazhar, Rashid
AuthorHamid, Tahir
AuthorIslam, Mohammad Tariqul
AuthorKashem, Saad
AuthorMahbub, Zaid Bin
AuthorAyari, Mohamed Arselene
AuthorChowdhury, Muhammad E. H.
Available date2023-04-17T06:57:48Z
Publication Date2020
Publication NameIEEE Access
ResourceScopus
URIhttp://dx.doi.org/10.1109/ACCESS.2020.3031384
URIhttp://hdl.handle.net/10576/42005
AbstractTuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis. 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
SponsorThis work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. Open Access funding provided by the Qatar National Library.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep learning
Image processing
Lungs segmentation
TB screening
Transfer learning
Tuberculosis detection
TitleReliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization
TypeArticle
Pagination191586-191601
Volume Number8
dc.accessType Open Access


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