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A New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection

Seng HansunSchool of Clinical Medicine, South West Sydney, UNSW Medicine and Health, UNSW Sydney, Sydney, NSW, AustraliaAhmadreza ArghaGraduate School of Biomedical Engineering, Tyree Institute of Health Engineering, Sydney, NSW, AustraliaHamid Alinejad‐RoknyBioMedical Machine Learning Lab, Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, AustraliaRoohallah AlizadehsaniInstitute for Intelligent Systems Research and Innovation, Deakin University, Burwood, VIC, AustraliaJ. M. GórrizDepartment of Signal Theory, Networking and Communications, Universidad de Granada, Granada, SpainSiaw‐Teng LiawSchool of Population Health, UNSW Sydney, Sydney, NSW, AustraliaBranko G. CellerBiomedical Systems Research Laboratory, School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, AustraliaGuy B. MarksSchool of Clinical Medicine, South West Sydney, UNSW Medicine and Health, UNSW Sydney, Sydney, NSW, Australia

2024en

ABI

Annotatsiya

Transfer learning (TL) is a strategic solution to handle vast data volume requirements in deep learning (DL). It transfers knowledge learned from a large base dataset, as a pretrained model (PTM), to a new domain. In this study, we introduce an ensemble of classifiers trained on features extracted from some intermediate layers of a PTM for Tuberculosis (TB) detection task. We use different EfficientNet variants: EfficientNet-B0–EfficientNet-B3, as the PTM. Moreover, we introduce a rejection mechanism and implement post-hoc calibration methods to enhance the reliability and trustworthiness of the developed models. Additionally, we conduct analyses on domain-shift distribution, a topic rarely discussed in the context of TB detection. Through a fivefold cross-validation on two prominent chest X-ray datasets, the Montgomery County (MC) and Shenzhen (SZ), our ensemble approach achieved competitive results with accuracies of 94.89% (MC) and 92.75% (SZ). The incorporation of the devised rejection mechanism resulted in enhanced model accuracy, albeit with a coverage tradeoff. In domain-shift experiments, the proposed approach achieved an accuracy of 83.57% (63% coverage) when applying the MC-trained model on SZ, and an accuracy of 88.50% (82% coverage) when applying the SZ-trained model on MC.

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Identifikatorlar

DOI: 10.1109/trpms.2024.3474708

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