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An end-to-end multi-task deep learning framework for bronchoscopy image classification

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Abstract

Lung cancer and tuberculosis (TB) are leading causes of mortality from lung diseases. Bronchoscopy plays a crucial role in diagnosing these conditions and determining appropriate treatment plans for patients. During bronchoscopy, clinicians often need to decide promptly whether to perform a lung biopsy upon observing abnormal symptoms. Since biopsies can lead to side effects such as excessive bleeding and infection, clinicians must make these decisions judiciously. Computer-aided diagnosis systems (CADx) can serve as virtual assistants, potentially preventing unnecessary procedures. This paper proposes a deep learning-based CADx system for diagnosing TB and lung cancer via bronchoscopy. Unlike normal and abnormal cases, lung cancer and TB are not easily distinguishable during bronchoscopy procedure. To address this challenge, a multi-task model with two branches, utilizing DenseNet and incorporating a Squeeze and Excitation (SE) module, is presented. Evaluated on a dataset of 515 images, the model achieved an impressive overall accuracy of 90.6%, surpassing a competing method. Sensitivities for cancer, TB, and normal cases were 91.3%, 81.5%, and 96.2%, respectively.

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No datasets were generated or analysed during the current study.

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Acknowledgements

The team acknowledges Dr. Y. Tang for sharing the research data and the clinical discussion.

Author information

Authors and Affiliations

  1. School of Mathematics and Computer Science, Iran University of Science and Technology, Tehran, Iran

    Rojin Setayeshi & Javad Vahidi

  2. Faculty of Technology and Engineering, University of Guilan, Rasht, Guilan, Iran

    Ehsan Kozegar

  3. Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China

    Tao Tan

Authors
  1. Rojin Setayeshi
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  2. Javad Vahidi
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  3. Ehsan Kozegar
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  4. Tao Tan
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Contributions

All authors conceived the study, designed the methodology, reviewed and approved the final manuscript. T.T. performed the data. R.S. developed the deep learning models, conducted the experiments, analyzed the experimental results, generated the visualizations and wrote the main manuscript text.

Corresponding author

Correspondence to Javad Vahidi.

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Setayeshi, R., Vahidi, J., Kozegar, E. et al. An end-to-end multi-task deep learning framework for bronchoscopy image classification. Multimedia Systems 30, 361 (2024). https://doi.org/10.1007/s00530-024-01579-3

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  • DOI: https://doi.org/10.1007/s00530-024-01579-3

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