The proposed study evaluates the efficacy of knowledge transfer gained through an ensemble of modality-specific deep learning models toward improving the state-of-the-art in Tuberculosis (TB) detection. A custom convolutional neural network (CNN) and selected popular pretrained CNNs are trained to learn modality-specific features from large-scale publicly available chest x-ray (CXR) collections including (i) RSNA dataset (normal = 8851, abnormal = 17833), (ii) Pediatric pneumonia dataset (normal = 1583, abnormal = 4273), and (iii) Indiana dataset (normal = 1726, abnormal = 2378). The knowledge acquired through modality-specific learning is transferred and fine-tuned for TB detection on the publicly available Shenzhen CXR collection (normal = 326, abnormal =336). The predictions of the best performing models are combined using different ensemble methods to demonstrate improved performance over any individual constituent model in classifying TB-infected and normal CXRs. The models are evaluated through cross-validation (n = 5) at the patient-level with an aim to prevent overfitting, improve robustness and generalization. It is observed that a stacked ensemble of the top-3 retrained models demonstrates promising performance (accuracy: 0.941; 95% confidence interval (CI): [0.899, 0.985], area under the curve (AUC): 0.995; 95% CI: [0.945, 1.00]). One-way ANOVA analyses show there are no statistically significant differences in accuracy ( = .759) and AUC ( = .831) among the ensemble methods. Knowledge transferred through modality-specific learning of relevant features helped improve the classification. The ensemble model resulted in reduced prediction variance and sensitivity to training data fluctuations. Results from their combined use are superior to the state-of-the-art.