TBDLNet: a network for classifying multidrug-resistant and
  drug-sensitive tuberculosis
Ziquan Zhu Jing Tao Shuihua Wang Xin Zhang Yudong Zhang
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.