Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection - Albadr, Musatafa Abbas Abbood; Ayob, Masri; Tiun, Sabrina; AL-Dhief, Fahad Taha; Hasan, Mohammad Kamrul (Frontiers in Public Health, 2022)
Many works employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that hits the human eye. However, the accuracy of most of the DR detection are still need more improvement. Grey Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the popular ML algorithms, which can be considered as an accurate algorithm in the process of classification but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA) to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it is not tested yet in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with Neural Network structure. Whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for the DR classification, and features extraction. The proposed GWO-ELM is evaluated based on two different datasets which are APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD) in both binary and multi-class classification. The experiments results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting the DR and might be applicable in solving other image data type.