DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only
Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing di...
Saved in:
| Main Authors: | Mohammad Tariqul Islam, Hamada R. H. Al-Absi, Essam A. Ruagh, Tanvir Alam |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9328261/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study
by: Hamada R. H. Al-Absi, et al.
Published: (2021-01-01) -
Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study
by: Aisha Ahmad M. A. Al-Khinji, et al.
Published: (2025-05-01) -
Distinctive blood and salivary proteomics signatures in Qatari individuals at high risk for cardiovascular disease
by: Ghada Yousif, et al.
Published: (2025-02-01) -
Association of serum magnesium and calcium with metabolic syndrome: a cross-sectional study from the Qatar-biobank
by: Raneem Alsheikh, et al.
Published: (2025-01-01) -
Red Meat Consumption, Iron Status, and Cardiometabolic Risk in Qatari Adults: A Cross-Sectional Gender-Stratified Analysis from the QPHI-QBB Data in Qatar
by: Hanaa Mousa, et al.
Published: (2025-06-01)