Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs
Tuberculosis (TB) is an infectious disease affecting humans’ lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a re...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/11/2335 |
| _version_ | 1797465222567428096 |
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| author | Evans Kotei Ramkumar Thirunavukarasu |
| author_facet | Evans Kotei Ramkumar Thirunavukarasu |
| author_sort | Evans Kotei |
| collection | DOAJ |
| description | Tuberculosis (TB) is an infectious disease affecting humans’ lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative for early TB screening. We propose an automatic TB detection system using advanced deep learning (DL) models. A substantial part of a CXR image is dark, with no relevant information for diagnosis and potentially confusing DL models. In this work, the U-Net model extracts the region of interest from CXRs and the segmented images are fed to the DL models for feature extraction. Eight different convolutional neural networks (CNN) models are employed in our experiments, and their classification performance is compared based on three publicly available CXR datasets. The U-Net model achieves segmentation accuracy of 98.58%, intersection over union (IoU) of 93.10, and a Dice coefficient score of 96.50. Our proposed stacked ensemble algorithm performed better by achieving accuracy, sensitivity, and specificity values of 98.38%, 98.89%, and 98.70%, respectively. Experimental results confirm that segmented lung CXR images with ensemble learning produce a better result than un-segmented lung CXR images. |
| first_indexed | 2024-03-09T18:18:26Z |
| format | Article |
| id | doaj.art-3ab06affb90149b1936ceca1ea01968a |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T18:18:26Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-3ab06affb90149b1936ceca1ea01968a2023-11-24T08:28:57ZengMDPI AGHealthcare2227-90322022-11-011011233510.3390/healthcare10112335Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray RadiographsEvans Kotei0Ramkumar Thirunavukarasu1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaTuberculosis (TB) is an infectious disease affecting humans’ lungs and is currently ranked the 13th leading cause of death globally. Due to advancements in technology and the availability of medical datasets, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative for early TB screening. We propose an automatic TB detection system using advanced deep learning (DL) models. A substantial part of a CXR image is dark, with no relevant information for diagnosis and potentially confusing DL models. In this work, the U-Net model extracts the region of interest from CXRs and the segmented images are fed to the DL models for feature extraction. Eight different convolutional neural networks (CNN) models are employed in our experiments, and their classification performance is compared based on three publicly available CXR datasets. The U-Net model achieves segmentation accuracy of 98.58%, intersection over union (IoU) of 93.10, and a Dice coefficient score of 96.50. Our proposed stacked ensemble algorithm performed better by achieving accuracy, sensitivity, and specificity values of 98.38%, 98.89%, and 98.70%, respectively. Experimental results confirm that segmented lung CXR images with ensemble learning produce a better result than un-segmented lung CXR images.https://www.mdpi.com/2227-9032/10/11/2335tuberculosis detectiondeep learningtransfer learningensemble learninglung segmentationmedical image analysis |
| spellingShingle | Evans Kotei Ramkumar Thirunavukarasu Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs Healthcare tuberculosis detection deep learning transfer learning ensemble learning lung segmentation medical image analysis |
| title | Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs |
| title_full | Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs |
| title_fullStr | Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs |
| title_full_unstemmed | Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs |
| title_short | Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs |
| title_sort | ensemble technique coupled with deep transfer learning framework for automatic detection of tuberculosis from chest x ray radiographs |
| topic | tuberculosis detection deep learning transfer learning ensemble learning lung segmentation medical image analysis |
| url | https://www.mdpi.com/2227-9032/10/11/2335 |
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