A New Ensemble Transfer Learning Approach with Rejection Mechanism for Tuberculosis Disease Detection Hansun, Seng Argha, Ahmadreza Alinejad Rokny, Hamid Alizadehsani, Roohallah Gorriz Sáez, Juan Manuel Liaw, Siaw Teng Celler, Branko G. Marks, Guy B. Convolutional neural networks Deep learning Domain-shift 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 Pre-Trained 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, B1, B2, and 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 5-fold 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 trade-off. 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. 2024-10-18T10:06:46Z 2024-10-18T10:06:46Z 2024-10-07 journal article S. Hansun et al., "A New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection," in IEEE Transactions on Radiation and Plasma Medical Sciences, doi: 10.1109/TRPMS.2024.3474708 https://hdl.handle.net/10481/96098 10.1109/TRPMS.2024.3474708 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Institute of Electrical and Electronics Engineers (IEEE)