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

Abstract

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.