Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images
Abstract Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagno...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49195-x |
| _version_ | 1797377039485894656 |
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| author | Sasikaladevi Natarajan Pradeepa Sampath Revathi Arunachalam Vimal Shanmuganathan Gaurav Dhiman Prasun Chakrabarti Tulika Chakrabarti Martin Margala |
| author_facet | Sasikaladevi Natarajan Pradeepa Sampath Revathi Arunachalam Vimal Shanmuganathan Gaurav Dhiman Prasun Chakrabarti Tulika Chakrabarti Martin Margala |
| author_sort | Sasikaladevi Natarajan |
| collection | DOAJ |
| description | Abstract Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist’s time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis. |
| first_indexed | 2024-03-08T19:47:17Z |
| format | Article |
| id | doaj.art-510b0e08e248472b922cc8aa3842823e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:17Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-510b0e08e248472b922cc8aa3842823e2023-12-24T12:15:29ZengNature PortfolioScientific Reports2045-23222023-12-0113111310.1038/s41598-023-49195-xEarly diagnosis and meta-agnostic model visualization of tuberculosis based on radiography imagesSasikaladevi Natarajan0Pradeepa Sampath1Revathi Arunachalam2Vimal Shanmuganathan3Gaurav Dhiman4Prasun Chakrabarti5Tulika Chakrabarti6Martin Margala7Department of Computer Science and Engineering, School of Computing, SASTRA Deemed UniversityDepartment of Information Technology, School of Computing, SASTRA Deemed UniversityDepartment of Electronics and Communications Engineering, School of EEE, SASTRA Deemed UniversityDeep Learning Lab, Department of Artificial Intelligence and Data Science, Ramco Institute of TechnologySchool of Sciences and Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open UniversitySir Padampat Singhania UniversitySir Padampat Singhania UniversityUniversity of Louisiana at LafayetteAbstract Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist’s time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.https://doi.org/10.1038/s41598-023-49195-x |
| spellingShingle | Sasikaladevi Natarajan Pradeepa Sampath Revathi Arunachalam Vimal Shanmuganathan Gaurav Dhiman Prasun Chakrabarti Tulika Chakrabarti Martin Margala Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images Scientific Reports |
| title | Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images |
| title_full | Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images |
| title_fullStr | Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images |
| title_full_unstemmed | Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images |
| title_short | Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images |
| title_sort | early diagnosis and meta agnostic model visualization of tuberculosis based on radiography images |
| url | https://doi.org/10.1038/s41598-023-49195-x |
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