Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning

September 10, 2018

Tao TanZhang LiHaixia LiuFarhad G. ZanjaniQuchang OuyangYuling TangZheyu HuQiang Li

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Abstract

Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
Bronchoscopy inspection, as a follow-up procedure next to the radiological imaging, plays a key role in the diagnosis and treatment design for lung disease patients. When performing bronchoscopy, doctors have to make a decision immediately whether to perform a biopsy. Because biopsies may cause uncontrollable and life-threatening bleeding of the lung tissue, thus doctors need to be selective with biopsies. In this work, to help doctors to be more selective on biopsies and provide a second opinion on diagnosis, we propose a computer-aided diagnosis (CAD) system for lung diseases, including cancers and tuberculosis (TB). Based on transfer learning (TL), we propose a novel TL method on the top of DenseNet: sequential fine-tuning (SFT). Compared to traditional fine-tuning (FT) methods, our method achieves the best performance. In a dataset of recruited 81 normal cases, 76 TB cases and 277 lung cancer cases, SFT provided an overall accuracy of 82while other traditional TL methods achieved an accuracy from 70to 74cases are 87the CAD system has potential to improve lung disease diagnosis accuracy in bronchoscopy and it may be used to be more selective with biopsies.

READ FULL ARTICLE ON IEEE XPLORE

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