  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. Moreover, the precision rate
of the PTB lesion type classification was 90.9%.The new method might serve as
an effective reference for decision making by clinical doctors.
