Machine Learning Approach for Detection of Diabetic Retinopathy with Improved Pre-Processing

Sharma, Ayushi; Shinde, Swapnil; Shaikh, Imran Ismail; Vyas, Madhav; Rani, Soumya

doi:10.1109/icccis51004.2021.9397115

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…Images of the retina captured with a fundus camera are used to assess DR. Image capture presents a number of difficulties [9], including light noise and low contrast, both of which negatively impact performance. Variations in size, shape, and colour make DR lesion segmentation a difficult process [10].…”

Section: Allenges In Fundus Imagesmentioning

confidence: 99%

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Jagadesh,

Karthik,

Siri

et al. 2023

IEEE Access

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Diabetic Retinopathy (DR) evaluations are increasingly being automated using artificial intelligence. Diabetes-related retinal vascular disease is a major cause of blindness and visual impairment worldwide. Therefore, automated DR detection devices would greatly aid in reducing visual impairment due to DR through early screening and treatment. Researchers have provided many techniques for picking out abnormalities in retinal images during the past several years. In the past, automated methods for diagnosing diabetic retinopathy required a human to extract information from retinal images before passing them on to a classifier. This study takes a novel two-pronged approach to automated DR classification to solve the issues. Due to the low positive instance percentage of existing asymmetric, we segment O.D.s and B.V.s with an enhanced version of an improved contoured convolutional transformer (IC2T). We develop a contoured optical disc (OD), a blood vessels (BV) detection module, and a dual convolutional transformer block that combines local and global contexts to make trustworthy associations. A second-stage Improved Coordination Attention Mechanism (ICAM) network is trained to recognize retinal biomarkers for DR such as microaneurysms (M.A.), haemorrhages (H.M.), and exudates (EX). With an average accuracy of 96%, 97%, and 98% on EyePACS-1, Messidor-2, and DIARETDB0, respectively, the suggested technique has proven itself to be at the field's cutting edge. Comprehensive testing and comparisons to established methods support the proposed strategy.

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Section: Allenges In Fundus Imagesmentioning

confidence: 99%

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Jagadesh,

Karthik,

Siri

et al. 2023

IEEE Access

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“…Standardization involves rescaling the features such that they have the properties of a standard normal distribution with a mean of zero and a standard deviation of one. The standard scaler is defined by the mathematical equation [54], [55], [56], [57], [58], [59], [60] given in equation (1).…”

Section: B Data Preprocessingmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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“…The method achieved a specificity of 83% and a sensitivity of 89%. Ayushi et al [17] proposed a image processing and machine learning methods for detecting DR for the DIARETDB dataset. The technique focused on improving the images, extracting features, and applying machine learning methods to classify retina images.…”

Section: Plos Onementioning

confidence: 99%

“…The table shows the overall accuracy of all systems. It is noted that the best accuracy was achieved when feeding the hybrid features of the [13], Fouzia et al [14], Gadekallu et al [15], Ludwig et al [16], Ayushi et al [17], Renukadevi et al [19]: These studies focused on various deep learning and machine learning approaches, using different architectures and techniques for retinopathy diagnosis. Specific performance metrics vary across the studies, but accuracies range from 65.6% to 96.6%.…”

Section: Plos Onementioning

confidence: 99%

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features

Shamsan,

Senan,

Ahmad Shatnawi

2023

PLoS ONE

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The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to blindness. Manual diagnosis requires effort and expertise and is prone to errors and differing expert diagnoses. Therefore, artificial intelligence techniques help doctors make a proper diagnosis and resolve different opinions. This study developed three approaches, each with two systems, for early diagnosis of DR disease progression. All colour fundus images have been subjected to image enhancement and increasing contrast ROI through filters. All features extracted by the DenseNet-121 and AlexNet (Dense-121 and Alex) were fed to the Principal Component Analysis (PCA) method to select important features and reduce their dimensions. The first approach is to DR image analysis for early prediction of DR disease progression by Artificial Neural Network (ANN) with selected, low-dimensional features of Dense-121 and Alex models. The second approach is to DR image analysis for early prediction of DR disease progression is by integrating important and low-dimensional features of Dense-121 and Alex models before and after PCA. The third approach is to DR image analysis for early prediction of DR disease progression by ANN with the radiomic features. The radiomic features are a combination of the features of the CNN models (Dense-121 and Alex) separately with the handcrafted features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP), Fuzzy colour histogram (FCH), and Gray Level Co-occurrence Matrix (GLCM) methods. With the radiomic features of the Alex model and the handcrafted features, ANN reached a sensitivity of 97.92%, an AUC of 99.56%, an accuracy of 99.1%, a specificity of 99.4% and a precision of 99.06%.

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Machine Learning Approach for Detection of Diabetic Retinopathy with Improved Pre-Processing

Cited by 18 publications

References 9 publications

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features

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