A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis

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We developed a deep learning model-based system to automatically generate a quantitative Computed Tomography (CT) diagnostic report for Pulmonary Tuberculosis (PTB) cases.501 CT imaging datasets from 223 patients with active PTB were collected, and another 501 cases from a healthy population served as negative samples.2884 lesions of PTB were carefully labeled and classified manually by professional radiologists.Three state-of-the-art 3D convolution neural network (CNN) models were trained and evaluated in the inspection of PTB CT images.

• Transfer learning method was also utilized during this process.
• The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma and cavitary types simultaneously.Then the Noisy-Or Bayesian function was used to generate an overall infection probability.Finally, a quantitative diagnostic report was exported.The results showed that the recall and precision rates, from the perspective of a single lesion region of PTB, were 85.9% and 89.2% respectively.
• The overall recall and precision rates,from the perspective of one PTB case, were 98.7% and 93.7%, respectively.