Deep Learning Approach for Automatic Microaneurysms Detection.
Sensors (Basel)
; 22(2)2022 Jan 11.
Article
in En
| MEDLINE
| ID: mdl-35062506
ABSTRACT
In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely "E-Ophtha" and "DIARETDB1", and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Diabetic Retinopathy
/
Microaneurysm
/
Deep Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
Sensors (Basel)
Year:
2022
Document type:
Article
Affiliation country:
Pakistan