A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Abstract

Diabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/healthtopics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lowerdimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes.

A Hybrid Deep Learning-Metaheuristic Model for Diagnosis of Diabetic Retinopathy

“…A GP regressor gives an AUC of 87.87% on Messidor-2. Gurcan et al [28] obtained feature representations from retina images using InceptionV3 pre-trained weights. The authors used Messidor-2 in training and testing.…”

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

“…The model is compared with baseline models such as EfficientNet B3, ResNet50 and DenseNet121. Gurcan et al [ 31 ] have proposed an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep CNN and ML methods. The model has extracted features from a pre-trained InceptionV3 model using transfer learning.…”

Detection algorithm for diabetic retinopathy based on ResNet and transfer learning

“…The algorithm adopts two layers of Init layer and Elite layer for feature extraction, combines genetic algorithm and softmax classifier, and uses truncated Newton constraint optimization method to train and fine-tune the layers in a supervised manner to obtain optimal weights. Gurcan [4] proposed a DR Classifier that integrated machine learning algorithms, which used machine learning methods and deep CNN (convolutional neural Network) for image preprocessing, feature extracting, and classification. Firstly, features were extracted from the pre-trained model by previous method, and then a variety of machine learning methods were used to classify DR Images.…”