DSEception: a noval neural networks architecture for enhancing pneumonia and tuberculosis diagnosis
Utilizing deep learning for automatic assistance in the diagnosis of pneumonia and tuberculosis holds clinical significance by enhancing diagnostic accuracy, reducing healthcare costs, enabling rapid screening and large-scale detection, and facilitating personalized treatment approaches, thereby contributing to widespread accessibility and improved healthcare services in the future.
Authors
- Guangliang Qiang (ORCID: https://orcid.org/0000-0002-7809-1892)
- Shengyi Li
- Yue Hu (ORCID: https://orcid.org/0000-0002-3846-9096)
- Xue Kong
- Baohua Lv
- Lexin Yang
Institutions
- Qingdao University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Beijing University of Technology (CN)
- Qingdao Municipal Hospital (CN)
- Peking University Third Hospital (CN)
- Guang’anmen Hospital (CN)
- Taian City Central Hospital (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2024-09-03
- DOI
- https://doi.org/10.3389/fbioe.2024.1454652
- Citations
- 7
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 2.83
Funders
- Peking University
- Peking University Third Hospital