ArticleIEEE journal of translational engineering in health and medicine2018
Optimize Transfer Learning for Lung Diseases in Bronchoscopy Using a New Concept: Sequential Fine-Tuning.
Article in IEEE journal of translational engineering in health and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 71 citations in OpenAlex.
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- An accurate prediction for respiratory diseases using deep learning on bronchoscopy diagnosis images.Journal of advanced research · 2025Article
- Diagnosing Respiratory Variability: Convolutional Neural Networks for Chest X-ray Classification Across Diverse Pulmonary Conditions.Journal of imaging informatics in medicine · 2025Article
- Review
- Tumor detection on bronchoscopic images by unsupervised learning.Scientific reports · 2025Article
- A Dual-Branch Residual Network with Attention Mechanisms for Enhanced Classification of Vaginal Lesions in Colposcopic Images.Bioengineering (Basel, Switzerland) · 2024Article
- Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.BMC medical imaging · 2024Article
- Deep Transfer Learning Method Using Self-Pixel and Global Channel Attentive Regularization.Sensors (Basel, Switzerland) · 2024Article
- A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.Scientific reports · 2024Article
- Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection.IEEE journal of translational engineering in health and medicine · 2023Article
- Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification.Translational lung cancer research · 2022Article
- A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer.Bioengineering (Basel, Switzerland) · 2022Article
- A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.Medical & biological engineering & computing · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning.Frontiers in human neuroscience · 2022Article
- The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer.Frontiers in oncology · 2022Article
- Clinical value of video-assisted single-lumen endotracheal intubation and application of artificial intelligence in it.American journal of translational research · 2022Review
- Lung Cancer Detection and Improving Accuracy Using Linear Subspace Image Classification Algorithm.Interdisciplinary sciences, computational life sciences · 2021Article
- Shock due to an Obstructed Endotracheal Tube.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2021Article
- Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.Journal of digital imaging · 2021Article
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Authors and funding
8 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.