A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning.
J Healthc Eng
; 2021: 1002799, 2021.
Article
in English
| MEDLINE | ID: covidwho-1571444
ABSTRACT
Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Tuberculosis
/
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Healthc Eng
Year:
2021
Document Type:
Article
Affiliation country:
2021
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