Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier. (1st May 2023)

• Record Type: Journal Article
• Authors: Nahiduzzaman, Md.; Robiul Islam, Md.; Omaer Faruq Goni, Md.; Shamim Anower, Md.; Ahsan, Mominul; Haider, Julfikar; Kowalski, Marcin
• Is Part Of: Expert systems with applications. Volume 217(2023)
• Publication Date: 2023-05-01
• Subjects: Contrast limited adaptive histogram equalization (CLAHE) -- Diabetic retinopathy (DR) -- Parallel convolutional neural network (PCNN) -- Extreme LEARNING MACHine (ELM); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2023.119557
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Diabetic retinopathy (DR) detection from Fundus Images has been proposed. Parallel CNN with fewer parameters and layers for distinctive feature extraction. Extreme Learning Machine (ELM) technique was utilized for the DR classification. The model demonstrated robustness for different types of datasets. Accuracy upto 97.27 % was achieved and outscored state-of-the-art models. Abstract: Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar that, if left untreated, can result in even blindness. A novel automated technique for DR detection has been proposed in this paper. To accentuate the lesions, the fundus images (FIs) were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). A parallel convolutional neural network (PCNN) was employed for feature extraction and then the extreme learning machine (ELM) technique was utilized for the DR classification. In comparison to the similar CNN structure, the PCNN design uses fewer parameters and layers, which minimizes the time required to extract distinctive features. The effectiveness of the technique was evaluated on two datasets (Kaggle DR 2015 competition (Dataset 1; 34, 984 FIs) and APTOS 2019 (3, 662 FIs)), and the results are promising. For the two datasets mentioned, the proposed technique attained accuracies of 91.78 % and 97.27 % respectively. However, one of the study's subsidiary discoveries was that the proposed framework demonstrated stability for both larger and smaller datasets, as well as for balanced and imbalanced datasets. Furthermore, in terms of classifier performance metrics, model parameters and layers, and prediction time, the suggested approach outscored existing state-of-the-art models, which would add significant benefit for the medical practitioners in accurately identifying the DR.