An Accurate Ensemble Classifier for Medical Volume Analysis: Phantom and Clinical PET Study
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2020.2975135
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 37482
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
1 - Intelligent Systems
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research addressed the real need for efficient approaches for positron emission tomography (PET) volume handling and the development of accurate and robust volume analysis approaches to aid clinicians in the clinical diagnosis and treatment planning. It was done in collaboration with an expert of advisory board, it aims to tackle the analysis problem in PET volumes for patients with squamous cell carcinoma. This paper gained national and international recognition from different universities. Such recognition has led to successful collaboration with UCL (I published new article with them) and keynote speaker invitation at an international conference (September 2020).
https://doi.org/10.1088/2057-1976/abc133
https://10times.com/iccsems-noida
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -