Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis classification.

Autor:	Sethanan K; Department of Industrial Engineer, Faculty of Engineering, Research Unit on System Modelling for Industry, Khon Kaen University, Khon Kaen, Thailand., Pitakaso R; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand., Srichok T; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand., Khonjun S; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand., Weerayuth N; Ubon Ratchathani University, Department of Mechanical Engineer, Faculty of Engineering, Ubon Ratchathani, Thailand., Prasitpuriprecha C; Division of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani, Thailand., Preeprem T; Division of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani, Thailand., Jantama SS; Ubon Ratchathani University, Division of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani, Thailand., Gonwirat S; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand.; Department of Computer Engineering and Automation, Faculty of Engineering, Kalasin University, Kalasin, Thailand., Enkvetchakul P; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand.; Department of Information Technology, Faculty of Sciences, Buriram Rajabhat University, Buriram, Thailand., Kaewta C; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand.; Department of Computer Science, Faculty of Computer Sciences, Ubon Ratchathani Rajabhat University, Ubon Ratchathani, Thailand., Nanthasamroeng N; Department of Industrial Engineer, Faculty of Engineering, Artificial Intelligence Optimization SMART Laboratory, Ubon Ratchathani University, Ubon Ratchathani, Thailand.; Department of Engineering Technology, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani, Thailand.
Jazyk:	angličtina
Zdroj:	Frontiers in medicine [Front Med (Lausanne)] 2023 Jun 26; Vol. 10, pp. 1122222. Date of Electronic Publication: 2023 Jun 26 (Print Publication: 2023).
DOI:	10.3389/fmed.2023.1122222
Abstrakt:	Introduction: This study aims to develop a web application, TB-DRD-CXR, for the categorization of tuberculosis (TB) patients into subgroups based on their level of drug resistance. The application utilizes an ensemble deep learning model that classifies TB strains into five subtypes: drug sensitive tuberculosis (DS-TB), drug resistant TB (DR-TB), multidrug-resistant TB (MDR-TB), pre-extensively drug-resistant TB (pre-XDR-TB), and extensively drug-resistant TB (XDR-TB). Methods: The ensemble deep learning model employed in the TB-DRD-CXR web application incorporates novel fusion techniques, image segmentation, data augmentation, and various learning rate strategies. The performance of the proposed model is compared with state-of-the-art techniques and standard homogeneous CNN architectures documented in the literature. Results: Computational results indicate that the suggested method outperforms existing methods reported in the literature, providing a 4.0%-33.9% increase in accuracy. Moreover, the proposed model demonstrates superior performance compared to standard CNN models, including DenseNet201, NASNetMobile, EfficientNetB7, EfficientNetV2B3, EfficientNetV2M, and ConvNeXtSmall, with accuracy improvements of 28.8%, 93.4%, 2.99%, 48.0%, 4.4%, and 7.6% respectively. Conclusion: The TB-DRD-CXR web application was developed and tested with 33 medical staff. The computational results showed a high accuracy rate of 96.7%, time-based efficiency (ET) of 4.16 goals/minutes, and an overall relative efficiency (ORE) of 100%. The system usability scale (SUS) score of the proposed application is 96.7%, indicating user satisfaction and a likelihood of recommending the TB-DRD-CXR application to others based on previous literature. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Sethanan, Pitakaso, Srichok, Khonjun, Weerayuth, Prasitpuriprecha, Preeprem, Jantama, Gonwirat, Enkvetchakul, Kaewta and Nanthasamroeng.)
Databáze:	MEDLINE
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