AUTHOR=Sethanan Kanchana , Pitakaso Rapeepan , Srichok Thanatkij , Khonjun Surajet , Weerayuth Nantawatana , Prasitpuriprecha Chutinun , Preeprem Thanawadee , Jantama Sirima Suvarnakuta , Gonwirat Sarayut , Enkvetchakul Prem , Kaewta Chutchai , Nanthasamroeng Natthapong TITLE=Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis classification JOURNAL=Frontiers in Medicine VOLUME=Volume 10 - 2023 YEAR=2023 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1122222 DOI=10.3389/fmed.2023.1122222 ISSN=2296-858X ABSTRACT=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.