Severity grading of diabetic retinopathy using extreme learning machine

Punithavathi, I. S. Hephzi; Kumar, P. Ganesh

doi:10.1109/itcosp.2017.8303088

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Authors extracted texture features using grey level co-occurrence matrices (GLCMs) to classify DR. Harini, and Sheela [11] applied Fuzzy C-Means clustering and some image processing operations (morphological) to extract features from exudates, blood vessels, and microaneurysms which are used as inputs for Support Vector Machines. Punithavathi and Kumar [12] detected the area of microaneurysms through image transformation, top hat transformation, and Otsu's thresholding. The authors also calculated statistical texture properties like mean and entropy.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Gürcan

Atıcı

Beyca

2023

Gazi University Journal of Science

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International Diabetes Federation (IDF) reports that diabetes is one of the rapidly growing illnesses. About 463 million adults between 20-79 years have diabetes. There are also millions of undiagnosed patients. It is estimated that there will be about 578 million diabetics by 2030 [1]. Diabetes reasons different eye diseases. Diabetic retinopathy (DR) is one of them and is also one of the most common vision loss or blindness worldwide. DR progresses slowly and has few indicators in the early stages. It makes the diagnosis of DR a problematic task. Automated systems promise to support the diagnosis of DR. Many deep learning-based models have been developed for DR classification. This study aims to support ophthalmologists in the diagnosis process and increase the diagnosis performance of DR through a hybrid model. A publicly available Messidor-2 dataset was used in this study, comprised of retinal images. In the proposed model, first, images were pre-processed and a deep learning model, namely, InceptionV3 was used in feature extraction where a transfer learning approach is applied. Next, the number of features in obtained feature vectors was decreased with feature selection by Simulated Annealing (SA). Lastly, the best representation features were used in XGBoost model. The XGBoost algorithm gives an accuracy of 92.26% in a binary classification task. This study shows that a pre-trained ConvNet with a metaheuristic algorithm for feature selection gives a satisfactory result in the diagnosis of DR.

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Section: Literature Reviewmentioning

confidence: 99%

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

Gürcan

Atıcı

Beyca

2023

Gazi University Journal of Science

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“…Further, Punithavathi and Kumar ( 25 ) used four different feature extraction techniques (i.e., mean, standard deviation, entropy, and third momentum) and the ELM classifier in order to detect DR. The proposed DR detection system was tested based on a multi-class classification problem using the DIARETDB0 dataset with four different classes.…”

Section: Related Workmentioning

confidence: 99%

“…Although (23)(24)(25)(26) showed that the ELM and KELM outperformed their comparatives, these studies have ignored the fact that the random generated input weights and biases of the ELM and KELM need to be optimized. In other words, there is no guarantee that the trained ELM/KELM is the best for carrying out the classification.…”

Section: Related Workmentioning

confidence: 99%

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

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Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and 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 has not yet been tested 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 a 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 DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment 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 DR and might be applicable in solving other image data types.

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“…Classification is made by taking into consideration the lesion locations or some statistical measures. Similarly, Punithavathi and Kumar [14] used morphological operations to extract the number of microaneurysms and texture features. These features are then classified by the Extreme Learning Machine classifier.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Gürcan¹,

Beyca²,

Doğan³

2021

IJCIS

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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.

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Severity grading of diabetic retinopathy using extreme learning machine

Cited by 6 publications

References 10 publications

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

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

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

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