Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence

In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease that can be fatal. Using advanced tools and technology, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative to the subjective assessment performed...

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Bibliographic Details
Published in:Neural computing & applications 2024-01, Vol.36 (1), p.111-131
Main Authors: Nafisah, Saad I., Muhammad, Ghulam
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Chest
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Explainable artificial intelligence
Image Processing and Computer Vision
Image segmentation
Infectious diseases
Lungs
Machine learning
Medical imaging
Multimedia
Neural networks
Probability and Statistics in Computer Science
S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics
Special Issue on Improving Healthcare outcomes using Multimedia Big Data Analytics
Subjective assessment
Technology assessment
Tuberculosis
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Summary:In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease that can be fatal. Using advanced tools and technology, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative to the subjective assessment performed by healthcare professionals. Thus, in the study, we propose an automatic TB detection system using advanced deep learning (DL) models. A significant portion of a CXR image is dark, providing no information for diagnosis and potentially confusing DL models. Therefore, in the proposed system, we use sophisticated segmentation networks to extract the region of interest from multimedia CXRs. Then, segmented images are fed into the DL models. For the subjective assessment, we use explainable artificial intelligence to visualize TB-infected parts of the lung. We use different convolutional neural network (CNN) models in our experiments and compare their classification performance using three publicly available CXR datasets. EfficientNetB3, one of the CNN models, achieves the highest accuracy of 99.1%, with a receiver operating characteristic of 99.9%, and an average accuracy of 98.7%. Experiment results confirm that using segmented lung CXR images produces better performance than does using raw lung CXR images.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07258-6