TBDLNet: A network for classifying multidrug-resistant and drug-sensitive tuberculosis

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.

Automated summary

This paper proposes a novel deep-learning model, TBDLNet, for automatically recognizing CT images and classifying multidrug-resistant and drug-sensitive tuberculosis. The model utilizes a pre-trained ResNet50 for feature extraction and employs three randomized neural networks to address overfitting. The ensemble of the three RNNs is applied for boosting robustness through majority voting. The model is evaluated using five-fold cross-validation and achieves high scores on accuracy, sensitivity, precision, F1-score, and specificity. TBDLNet is suitable for classifying multidrug-resistant and drug-sensitive tuberculosis, and allows for early detection of multidrug-resistant pulmonary tuberculosis, enabling timely adjustments to treatment plans and improved treatment outcomes.