  Bronchoscopy inspection as a follow-up procedure from the radiological
imaging plays a key role in lung disease diagnosis and determining treatment
plans for the patients. Doctors needs to make a decision whether to biopsy the
patients timely when performing bronchoscopy. However, the doctors also needs
to be very selective with biopsies as biopsies may cause uncontrollable
bleeding of the lung tissue which is life-threaten. To help doctors to be more
selective on biopsies and provide a second opinion on diagnosis, in this work,
we propose a computer-aided diagnosis (CAD) system for lung diseases including
cancers and tuberculosis (TB). The system is developed based on transfer
learning. We propose a novel transfer learning method: sentential fine-tuning .
Compared to traditional fine-tuning methods, our methods achieves the best
performance. We obtained a overall accuracy of 77.0% a dataset of 81 normal
cases, 76 tuberculosis cases and 277 lung cancer cases while the other
traditional transfer learning methods achieve an accuracy of 73% and 68%. . The
detection accuracy of our method for cancers, TB and normal cases are 87%, 54%
and 91% respectively. This indicates that the CAD system has potential to
improve lung disease diagnosis accuracy in bronchoscopy and it also might be
used to be more selective with biopsies.
