| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01292 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001292 | |
| Published online | 06 October 2023 | |
Deep learning system for assessing diabetic retinopathy prevalence and risk level estimation
1 Tripura Institute of Technology, Narsingarh, West Tripura, 799009, India.
2 ICFAI University Tripura, Kamalghat, West Tripura, 799210, India.
* Corresponding author: abiswas.tit@gmail.com
Diabetic retinopathy, one of the foremost problems brought on by Diabetes Mellitus has seen an exponential rise in incidence due to the exponential growth of diabetics worldwide and causes visual issues and sightlessness owing to deformity of individual retina. An early detection and diagnosis are necessary to stop DR from progressing into severe stages and to stop blindness for which regular screening of eye is mandatory. To do this, several machine learning (ML) models are available. However, when used with bigger datasets, classical ML models either need more training time and have less generalisation in feature extraction and classification than when used with smaller data volumes. As a result, Deep Learning (DL), a newer ML paradigm that can manage a relatively small data volume with aid of effective data processing methods is presented. They do, still, often use bigger data in the deep network structure to improve feature extraction and picture classification performance. This study presents a CNN model for DR classification and compares with other variants of pre-trained DL models for initial recognition of DR through binary and multi-class classification. The attained result of 97% accuracy reveals that pre-trained ResNet model’s efficacy is better in diagnosing DR.
Key words: Diabetic retinopathy / Deep learning / CNN / ResNet / fundus images
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.