Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System
This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/16/1/13 |
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| author | Chutinun Prasitpuriprecha Sirima Suvarnakuta Jantama Thanawadee Preeprem Rapeepan Pitakaso Thanatkij Srichok Surajet Khonjun Nantawatana Weerayuth Sarayut Gonwirat Prem Enkvetchakul Chutchai Kaewta Natthapong Nanthasamroeng |
| author_facet | Chutinun Prasitpuriprecha Sirima Suvarnakuta Jantama Thanawadee Preeprem Rapeepan Pitakaso Thanatkij Srichok Surajet Khonjun Nantawatana Weerayuth Sarayut Gonwirat Prem Enkvetchakul Chutchai Kaewta Natthapong Nanthasamroeng |
| author_sort | Chutinun Prasitpuriprecha |
| collection | DOAJ |
| description | This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10. |
| first_indexed | 2024-03-09T11:29:25Z |
| format | Article |
| id | doaj.art-4e9dcb703e6e463cad3168753886bd3b |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-09T11:29:25Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-4e9dcb703e6e463cad3168753886bd3b2023-11-30T23:54:33ZengMDPI AGPharmaceuticals1424-82472022-12-011611310.3390/ph16010013Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based SystemChutinun Prasitpuriprecha0Sirima Suvarnakuta Jantama1Thanawadee Preeprem2Rapeepan Pitakaso3Thanatkij Srichok4Surajet Khonjun5Nantawatana Weerayuth6Sarayut Gonwirat7Prem Enkvetchakul8Chutchai Kaewta9Natthapong Nanthasamroeng10Department of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Mechanical Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, ThailandDepartment of Computer Engineering and Automation, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, ThailandDepartment of Information Technology, Faculty of Science, Buriram University, Buriram 31000, ThailandDepartment of Computer Science, Faculty of Computer Science, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, ThailandDepartment of Engineering Technology, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, ThailandThis research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.https://www.mdpi.com/1424-8247/16/1/13ensemble deep learningmulticlass-AMISchest X-raydrug-resistanttuberculosisweb application diagnosis |
| spellingShingle | Chutinun Prasitpuriprecha Sirima Suvarnakuta Jantama Thanawadee Preeprem Rapeepan Pitakaso Thanatkij Srichok Surajet Khonjun Nantawatana Weerayuth Sarayut Gonwirat Prem Enkvetchakul Chutchai Kaewta Natthapong Nanthasamroeng Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System Pharmaceuticals ensemble deep learning multiclass-AMIS chest X-ray drug-resistant tuberculosis web application diagnosis |
| title | Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System |
| title_full | Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System |
| title_fullStr | Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System |
| title_full_unstemmed | Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System |
| title_short | Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System |
| title_sort | drug resistant tuberculosis treatment recommendation and multi class tuberculosis detection and classification using ensemble deep learning based system |
| topic | ensemble deep learning multiclass-AMIS chest X-ray drug-resistant tuberculosis web application diagnosis |
| url | https://www.mdpi.com/1424-8247/16/1/13 |
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