Two-stage classification of tuberculosis culture diagnosis using convolutional neural network with transfer learning

Tuberculosis (TB) has been one of top 10 leading causes of death. A computer-aided diagnosis system to accelerate TB diagnosis is crucial. In this paper, we apply convolutional neural network and deep learning to classify the images of TB culture test-the gold standard of TB diagnostic test. Since the dataset is small and imbalanced, a transfer learning approach is applied. Moreover, as the recall of non-negative class is an important metric for this application, we propose a two-stage classification method to boost the results. The experiment results on a real dataset of TB culture test (1727 samples with 16,503 images from Tao-Yuan General Hospital, Taiwan) show that the proposed method can achieve 99% precision and 98% recall on the non-negative class.