Cargando…
Deep learning-based automatic detection of tuberculosis disease in chest X-ray images
PURPOSE: To train a convolutional neural network (CNN) model from scratch to automatically detect tuberculosis (TB) from chest X-ray (CXR) images and compare its performance with transfer learning based technique of different pre-trained CNNs. MATERIAL AND METHODS: We used two publicly available dat...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Termedia Publishing House
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906182/ https://www.ncbi.nlm.nih.gov/pubmed/35280947 http://dx.doi.org/10.5114/pjr.2022.113435 |
_version_ | 1784665354150084608 |
---|---|
author | Showkatian, Eman Salehi, Mohammad Ghaffari, Hamed Reiazi, Reza Sadighi, Nahid |
author_facet | Showkatian, Eman Salehi, Mohammad Ghaffari, Hamed Reiazi, Reza Sadighi, Nahid |
author_sort | Showkatian, Eman |
collection | PubMed |
description | PURPOSE: To train a convolutional neural network (CNN) model from scratch to automatically detect tuberculosis (TB) from chest X-ray (CXR) images and compare its performance with transfer learning based technique of different pre-trained CNNs. MATERIAL AND METHODS: We used two publicly available datasets of postero-anterior chest radiographs, which are from Montgomery County, Maryland, and Shenzhen, China. A CNN (ConvNet) from scratch was trained to automatically detect TB on chest radiographs. Also, a CNN-based transfer learning approach using five different pre-trained models, including Inception_v3, Xception, ResNet50, VGG19, and VGG16 was utilized for classifying TB and normal cases from CXR images. The performance of models for testing datasets was evaluated using five performances metrics, including accuracy, sensitivity/recall, precision, area under curve (AUC), and F1-score. RESULTS: All proposed models provided an acceptable accuracy for two-class classification. Our proposed CNN architecture (i.e., ConvNet) achieved 88.0% precision, 87.0% sensitivity, 87.0% F1-score, 87.0% accuracy, and AUC of 87.0%, which was slightly less than the pre-trained models. Among all models, Exception, ResNet50, and VGG16 provided the highest classification performance of automated TB classification with precision, sensitivity, F1-score, and AUC of 91.0%, and 90.0% accuracy. CONCLUSIONS: Our study presents a transfer learning approach with deep CNNs to automatically classify TB and normal cases from the chest radiographs. The classification accuracy, precision, sensitivity, and F1-score for the detection of TB were found to be more than 87.0% for all models used in the study. Exception, ResNet50, and VGG16 models outperformed other deep CNN models for the datasets with image augmentation methods. |
format | Online Article Text |
id | pubmed-8906182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Termedia Publishing House |
record_format | MEDLINE/PubMed |
spelling | pubmed-89061822022-03-11 Deep learning-based automatic detection of tuberculosis disease in chest X-ray images Showkatian, Eman Salehi, Mohammad Ghaffari, Hamed Reiazi, Reza Sadighi, Nahid Pol J Radiol Original Paper PURPOSE: To train a convolutional neural network (CNN) model from scratch to automatically detect tuberculosis (TB) from chest X-ray (CXR) images and compare its performance with transfer learning based technique of different pre-trained CNNs. MATERIAL AND METHODS: We used two publicly available datasets of postero-anterior chest radiographs, which are from Montgomery County, Maryland, and Shenzhen, China. A CNN (ConvNet) from scratch was trained to automatically detect TB on chest radiographs. Also, a CNN-based transfer learning approach using five different pre-trained models, including Inception_v3, Xception, ResNet50, VGG19, and VGG16 was utilized for classifying TB and normal cases from CXR images. The performance of models for testing datasets was evaluated using five performances metrics, including accuracy, sensitivity/recall, precision, area under curve (AUC), and F1-score. RESULTS: All proposed models provided an acceptable accuracy for two-class classification. Our proposed CNN architecture (i.e., ConvNet) achieved 88.0% precision, 87.0% sensitivity, 87.0% F1-score, 87.0% accuracy, and AUC of 87.0%, which was slightly less than the pre-trained models. Among all models, Exception, ResNet50, and VGG16 provided the highest classification performance of automated TB classification with precision, sensitivity, F1-score, and AUC of 91.0%, and 90.0% accuracy. CONCLUSIONS: Our study presents a transfer learning approach with deep CNNs to automatically classify TB and normal cases from the chest radiographs. The classification accuracy, precision, sensitivity, and F1-score for the detection of TB were found to be more than 87.0% for all models used in the study. Exception, ResNet50, and VGG16 models outperformed other deep CNN models for the datasets with image augmentation methods. Termedia Publishing House 2022-02-28 /pmc/articles/PMC8906182/ /pubmed/35280947 http://dx.doi.org/10.5114/pjr.2022.113435 Text en © Pol J Radiol 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0). License (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Original Paper Showkatian, Eman Salehi, Mohammad Ghaffari, Hamed Reiazi, Reza Sadighi, Nahid Deep learning-based automatic detection of tuberculosis disease in chest X-ray images |
title | Deep learning-based automatic detection of tuberculosis disease in chest X-ray images |
title_full | Deep learning-based automatic detection of tuberculosis disease in chest X-ray images |
title_fullStr | Deep learning-based automatic detection of tuberculosis disease in chest X-ray images |
title_full_unstemmed | Deep learning-based automatic detection of tuberculosis disease in chest X-ray images |
title_short | Deep learning-based automatic detection of tuberculosis disease in chest X-ray images |
title_sort | deep learning-based automatic detection of tuberculosis disease in chest x-ray images |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906182/ https://www.ncbi.nlm.nih.gov/pubmed/35280947 http://dx.doi.org/10.5114/pjr.2022.113435 |
work_keys_str_mv | AT showkatianeman deeplearningbasedautomaticdetectionoftuberculosisdiseaseinchestxrayimages AT salehimohammad deeplearningbasedautomaticdetectionoftuberculosisdiseaseinchestxrayimages AT ghaffarihamed deeplearningbasedautomaticdetectionoftuberculosisdiseaseinchestxrayimages AT reiazireza deeplearningbasedautomaticdetectionoftuberculosisdiseaseinchestxrayimages AT sadighinahid deeplearningbasedautomaticdetectionoftuberculosisdiseaseinchestxrayimages |