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The distinct morphological characteristics of chest abnormalities between drug\u2010resistant tuberculosis (DR\u2010TB) and drug\u2010sensitive tuberculosis (DS\u2010TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning\u2010based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR\u2010TB and classify crucial subtypes, including rifampicin\u2010resistant tuberculosis, multidrug\u2010resistant tuberculosis, and extensively drug\u2010resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR\u2010TB. 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