This paper proposes applying a novel deep-learning model, TBDLNet, to recognize CT images to classify multidrug-resistant and drug-sensitive tuberculosis automatically. The pre-trained ResNet50 is selected to extract features. Three randomized neural networks are used to alleviate the overfitting problem. The ensemble of three RNNs is applied to boost the robustness via majority voting. The proposed model is evaluated by five-fold cross-validation. Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1-score, and specificity. The TBDLNet achieves 0.9822 accuracy, 0.9815 specificity, 0.9823 precision, 0.9829 sensitivity, and 0.9826 F1-score, respectively. The TBDLNet is suitable for classifying multidrug-resistant tuberculosis and drug-sensitive tuberculosis. It can detect multidrug-resistant pulmonary tuberculosis as early as possible, which helps to adjust the treatment plan in time and improve the treatment effect.

翻译：本文提出应用新型深度学习模型TBDLNet识别CT图像，以实现耐多药与药物敏感性肺结核的自动分类。采用预训练ResNet50进行特征提取，通过三种随机神经网络缓解过拟合问题，并集成三种RNN利用多数投票机制增强模型鲁棒性。采用五折交叉验证评估模型性能，选取准确率、灵敏度、精确率、F1分数和特异度五项指标。TBDLNet分别达到0.9822准确率、0.9815特异度、0.9823精确率、0.9829灵敏度和0.9826 F1分数。该模型适用于区分耐多药与药物敏感性肺结核，可尽早检测耐多药肺结核，有助于及时调整治疗方案并提升治疗效果。