Affiliations: [a] Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai | [b] Department of ECE, SSN College of Engineering, Kalavakkam, Chennai | [b] Department of ECE, Hindusthan Institute of Technology, Coimbatore
Correspondence:
[*]
Corresponding author. K.J. Subha, Assistant Professor, Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai. E-mail: [email protected].
Abstract: The Retinal image analysis has received significant attention from researchers due to the compelling need of early detection systems that aid in the screening and treatment of diseases. Several automated retinal disease detection studies are carried out as part of retinal image processing. Heren an Improved Ensemble Deep Learning (IEDL) model has been proposed to detect the various retinal diseases with a higher rate of accuracy, having multiclass classification on various stages of deep learning algorithms. This model incorporates deep learning algorithms which automatically extract the properties from training data, that lacks in traditional machine learning approaches. Here, Retinal Fundus Multi-Disease Image Dataset (RFMiD) is considered for evaluation. First, image augmentation is performed for manipulating the existing images followed by upsampling and normalization. The proposed IEDL model then process the normalized images which is computationally intensive with several ensemble learning strategies like heterogeneous deep learning models, bagging through 5-fold cross-validation which consists of four deep learning models like ResNet, Bagging, DenseNet, EfficientNet and a stacked logistic regression for predicting purpose. The accuracy rate achieved by this method is 97.78%, with a specificity rate of 97.23%, sensitivity of 96.45%, precision of 96.45%, and recall of 94.23%. The model is capable of achieving a greater accuracy rate of 1.7% than the traditional machine learning methods.
Keywords: Improved Ensemble Deep learning (IEDL), bagging through 5-fold cross-validation, Retinal Fundus Multi-Disease Image Dataset (RFMiD), Stacked logistic regression
