Gastro-BaseNet: A Specialized Pre-Trained Model for Enhanced Gastroscopic Data Classification and Diagnosis of Gastric Cancer and Ulceropen access
- Authors
- Lee, Gi Pyo; Kim, Young Jae; Park, Dong Kyun; Kim, Yoon Jae; Han, Su Kyeong; Kim, Kwang Gi
- Issue Date
- Jan-2024
- Publisher
- MDPI
- Keywords
- gastroscopy; transfer learning; deep learning; Gastro-BaseNet; ImageNet; endoscopy
- Citation
- DIAGNOSTICS, v.14, no.1
- Journal Title
- DIAGNOSTICS
- Volume
- 14
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90378
- DOI
- 10.3390/diagnostics14010075
- ISSN
- 2075-4418
2075-4418
- Abstract
- Most of the development of gastric disease prediction models has utilized pre-trained models from natural data, such as ImageNet, which lack knowledge of medical domains. This study proposes Gastro-BaseNet, a classification model trained using gastroscopic image data for abnormal gastric lesions. To prove performance, we compared transfer-learning based on two pre-trained models (Gastro-BaseNet and ImageNet) and two training methods (freeze and fine-tune modes). The effectiveness was verified in terms of classification at the image-level and patient-level, as well as the localization performance of lesions. The development of Gastro-BaseNet had demonstrated superior transfer learning performance compared to random weight settings in ImageNet. When developing a model for predicting the diagnosis of gastric cancer and gastric ulcers, the transfer-learned model based on Gastro-BaseNet outperformed that based on ImageNet. Furthermore, the model's performance was highest when fine-tuning the entire layer in the fine-tune mode. Additionally, the trained model was based on Gastro-BaseNet, which showed higher localization performance, which confirmed its accurate detection and classification of lesions in specific locations. This study represents a notable advancement in the development of image analysis models within the medical field, resulting in improved diagnostic predictive accuracy and aiding in making more informed clinical decisions in gastrointestinal endoscopy.
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