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

Tao Tan, Zhang Li, Haixia Liu, Farhad Ghazvinian Zanjani, Quchang Ouyang, Yuling Tang, Zheyu Hu, Qiang Li

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)
51 Downloads (Pure)

Abstract

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 paper, 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 with traditional fine-tuning (FT) methods, our method achieves the best performance. In a data set of recruited 81 normal cases, 76 TB cases and 277 lung cancer cases, SFT provided an overall accuracy of 82% while other traditional TL methods achieved an accuracy from 70% to 74%. The detection accuracy of SFT for cancers, TB, and normal cases are 87%, 54%, and 91%, respectively. This indicates that the CAD system has the potential to improve lung disease diagnosis accuracy in bronchoscopy and it may be used to be more selective with biopsies.

Original languageEnglish
Article number1800808
Pages (from-to)1800808
Number of pages8
JournalIEEE Journal of Translational Engineering in Health and Medicine
Volume6
DOIs
Publication statusPublished - 16 Aug 2018

Keywords

  • Bronchoscopy
  • computer-aided diagnosis
  • deep learning
  • DenseNet
  • lung cancer
  • sequential finetuning
  • transfer learning
  • tuberculosis

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