Back to Search Start Over

Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy

Authors :
Inamullah
Saima Hassan
Nabil A. Alrajeh
Emad A. Mohammed
Shafiullah Khan
Source :
Biomimetics, Vol 8, Iss 2, p 187 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and classify stages of diabetic retinopathy (DR). DR is a chronic disease that appears in patients with long-term diabetes. Unattained patients can lead to severe conditions of DR, such as retinal eye detachments. Therefore, early detection and classification of DR are crucial to ward off advanced stages of DR and preserve the vision. Data diversity in an ensemble model refers to the use of multiple models trained on different subsets of data to improve the ensemble’s overall performance. In the context of an ensemble model based on a convolutional neural network (CNN) for diabetic retinopathy, this could involve training multiple CNNs on various subsets of retinal images, including images from different patients or those captured using distinct imaging techniques. By combining the predictions of these multiple models, the ensemble model can potentially make more accurate predictions than a single prediction. In this paper, an ensemble model (EM) of three CNN models is proposed for limited and imbalanced DR data using data diversity. Detecting the Class 1 stage of DR is important to control this fatal disease in time. CNN-based EM is incorporated to classify the five classes of DR while giving attention to the early stage, i.e., Class 1. Furthermore, data diversity is created by applying various augmentation and generation techniques with affine transformation. Compared to the single model and other existing work, the proposed EM has achieved better multi-class classification accuracy, precision, sensitivity, and specificity of 91.06%, 91.00%, 95.01%, and 98.38%, respectively.

Details

Language :
English
ISSN :
23137673
Volume :
8
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Biomimetics
Publication Type :
Academic Journal
Accession number :
edsdoj.33bb4e43ed0d45e5959e3016c667dc4d
Document Type :
article
Full Text :
https://doi.org/10.3390/biomimetics8020187