Gastro-BaseNet: A Specialized Pre-Trained Model for Enhanced Gastroscopic Data Classification and Diagnosis of Gastric Cancer and Ulcer
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Gi Pyo | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Park, Dong Kyun | - |
dc.contributor.author | Kim, Yoon Jae | - |
dc.contributor.author | Han, Su Kyeong | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.date.accessioned | 2024-02-12T00:30:37Z | - |
dc.date.available | 2024-02-12T00:30:37Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 2075-4418 | - |
dc.identifier.issn | 2075-4418 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90378 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Gastro-BaseNet: A Specialized Pre-Trained Model for Enhanced Gastroscopic Data Classification and Diagnosis of Gastric Cancer and Ulcer | - |
dc.type | Article | - |
dc.identifier.wosid | 001139313400001 | - |
dc.identifier.doi | 10.3390/diagnostics14010075 | - |
dc.identifier.bibliographicCitation | DIAGNOSTICS, v.14, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85181968097 | - |
dc.citation.title | DIAGNOSTICS | - |
dc.citation.volume | 14 | - |
dc.citation.number | 1 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | gastroscopy | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | Gastro-BaseNet | - |
dc.subject.keywordAuthor | ImageNet | - |
dc.subject.keywordAuthor | endoscopy | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.