AB093. Pixel-wise classification of glioma using deep learning for accurate tumour mapping on magnetic resonance imaging
With the advent of technology, AI continues to modify our lifestyles. It is critical to adapt this technology in healthcare with the aim of improving the provision of patient care. We present our preliminary data for the use of ML algorithms in the diagnosis and segmentation of glioma. The promising result with comparable accuracy highlights the importance of early adaptation of this nascent technology.
Authors
- Komal Naeem (ORCID: https://orcid.org/0009-0005-1192-1934)
- Manal Nasir
- Rabeet Tariq (ORCID: https://orcid.org/0000-0002-4281-7436)
- Nouman Mughal (ORCID: https://orcid.org/0000-0002-3298-3959)
- Salma Asif
- Meher Angez (ORCID: https://orcid.org/0000-0002-4490-4453)
- Ummul Wara
- Syed Ather Enam (ORCID: https://orcid.org/0000-0003-2194-0723)
- Kiran Aftab
- Fatima Mubarak (ORCID: https://orcid.org/0000-0003-4021-2676)
- Ahmad Raza Shahid
- Zeba Saleem
- Faryal Raees
- Ceemal Fareed
- Muhammad Sameer
- Amna Farrukh
- Ansar Rahman
Institutions
- National University of Computer and Emerging Sciences (PK)
Publication Details
- Journal
- Chinese Clinical Oncology
- Published
- 2024-08-01
- DOI
- https://doi.org/10.21037/cco-24-ab093
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.25