Design of Liver Cancer Diagnosis System with Structured Database
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. |
| Pages | 1187-1192 |
| ISBN (electronic) | 978-1-6654-9079-5 |
| ISBN (print) | 978-1-6654-9080-1 |
| Publication status | Published - 2023 |
Publication series
| Name | Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA |
|---|
Conference
| Title | 3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA 2023) |
|---|---|
| Place | China |
| City | Chongqing |
| Period | 26 - 28 May 2023 |
Link(s)
Abstract
The liver, the largest organ in the human abdomen, possesses a complex structure. Clinicians can diagnosis liver cancer by computed tomography (CT) images. Early detection and treatment of liver cancer are imperative to avoid significant complications. Unfortunately, traditional diagnostic techniques are time-consuming and heavily reliant on expert interpretation. Consequently, there is a pressing need for an automated and comprehensive system to replace conventional diagnostic methods. Also, there are numerous organizations that voluntarily share liver and liver tumor datasets all around the internet, and a central platform is required to combine various data sources, well organize the data, and enhance re-usability. There are a few studies on image intelligent diagnosis system and they all focus on liver tumor detection but omitted organizing the data well. In this research, we designed a liver cancer diagnosis system with structured database. And we have proposed a novel flow of data warehousing, data annotation and liver cancer diagnosis for computer-aided diagnosis. © 2023 IEEE.
Research Area(s)
- deep learning, image segmentation, liver cancer diagnosis system, structured database
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Design of Liver Cancer Diagnosis System with Structured Database. / Wang, Kuanglan; Chan, Kwan Ho; Liu, Engui et al.
2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 1187-1192 (Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA).
2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 1187-1192 (Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
