Name
Title | ||
|---|---|---|
A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection. |
Abstract | ||
|---|---|---|
In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-97982-3_14 | ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI) |
Keywords | Field | DocType |
Machine learning,Endoscopy,Cancer detection,Texture analysis division | Early detection,Endoscopy,Image processing,Cancer detection,Adenocarcinoma,Radiology,Colorectal cancer,Medicine,Cancer | Conference |
Volume | ISSN | Citations |
840 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheqi Yu | 1 | 0 | 0.34 |
| Shufan Yang | 2 | 109 | 15.18 |
| Keliang Zhou | 3 | 585 | 52.17 |
| Amar Aggoun | 4 | 115 | 21.34 |