Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network

Minarno, Agus Eko; Mandiri, Mochammad Hazmi Cokro; Azhar, Yufis; Bimantoro, Fitri; Nugroho, Hanung Adi; İbrahim, Zarina Bibi

doi:10.30630/joiv.6.1.857

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…Moreover, our validation accuracy gives better results with a score of 95.33% compared to Bodapati et al [8,19] with 80.96% validation accuracy and Patel and Chaware [13] with 81.00% accuracy. Our work also gives better training accuracy results when compared to Minarno et al [23] work. Although their research gives better test and F1-Score results, Minarno et al [23] research uses a different proportion of training and validation compared to our work, and they use the EfficientNet-B7 model which has more parameters compared to the base EfficientNet-B0 model, thus needing more computational power, resulting with a model that is bigger in sizes.…”

Section: Testing Resultsmentioning

confidence: 49%

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Handoyo¹,

Kusuma²

2022

RIA

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Diabetic retinopathy (DR), is a complication resulting from the disease that can lead to blindness if not detected early. Recently, many classification systems for diabetic retinopathy have been developed. However, several problems were found, namely, the classification results in certain classes still have less than optimal accuracy values, the lack of in-depth analysis for the results, and the overall accuracy that can still be improved. In this work, we experiment by evaluating and combining new deep learning models such as EfficientNet, EfficientNetV2, LCNet, MobileNetV3, TinyNet, and FBNetV3 using ensemble stacking techniques with four different meta-learners: decision trees, logistic regression, ANN, and SVM to provide better accuracy in classifying the severity of diabetic retinopathy. Our work offers satisfactory classification results on the APTOS 2019 dataset with training, validation, testing, and F1 score accuracy of 96.56%, 95.33%, 84.17%, and 70.16%, respectively.

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Section: Testing Resultsmentioning

confidence: 49%

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Handoyo¹,

Kusuma²

2022

RIA

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“…Quite a few attention mechanism networks [29][30][31][32] and ensemble algorithms [33,34] have also been applied to DR diagnosis. Moreover, several recent studies [35][36][37] have employed more advanced CNN models or introduced additional auxiliary models to enhance the grading of DR. And some approaches [38,39] have utilized a multi-stage training methodology to progressively improve the model's performance. All these methods have involved good innovations in the structure of the model and achieved the state-of-the-art results at that time.…”

Section: Related Workmentioning

confidence: 99%

Supervised Contrastive Learning with Angular Margin for the Detection and Grading of Diabetic Retinopathy

Zhu

Chen

et al. 2023

Diagnostics

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Many researchers have realized the intelligent medical diagnosis of diabetic retinopathy (DR) from fundus images by using deep learning methods, including supervised contrastive learning (SupCon). However, although SupCon brings label information into the calculation of contrastive learning, it does not distinguish between augmented positives and same-label positives. As a result, we propose the concept of Angular Margin and incorporate it into SupCon to address this issue. To demonstrate the effectiveness of our strategy, we tested it on two datasets for the detection and grading of DR. To align with previous work, Accuracy, Precision, Recall, F1, and AUC were selected as evaluation metrics. Moreover, we also chose alignment and uniformity to verify the effect of representation learning and UMAP (Uniform Manifold Approximation and Projection) to visualize fundus image embeddings. In summary, DR detection achieved state-of-the-art results across all metrics, with Accuracy = 98.91, Precision = 98.93, Recall = 98.90, F1 = 98.91, and AUC = 99.80. The grading also attained state-of-the-art results in terms of Accuracy and AUC, which were 85.61 and 93.97, respectively. The experimental results demonstrate that Angular Margin is an excellent intelligent medical diagnostic algorithm, performing well in both DR detection and grading tasks.

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“…Diabetic disease needs a unique study to improve the availability of investigation or reduce the variance and spread of this vital disease. Studies are becoming more sensitive to such an issue due to its causes and symptoms, as discussed and explained in [40] and [41].…”

Section: F Features Selection Techniquesmentioning

confidence: 99%

Feature Minimization for Diabetic Disorders High Performances Prediction System-based on Random Forest Tree

Mohammed,

Ahmed,

Mohammed

2023

JOIV : Int. J. Inform. Visualization

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Human organ failure due to high blood sugar is considered a chronic disease. Early prediction might reduce or prevent complications due to such disorders, especially with recent machine-learning improvement techniques and the availability of electronic data from different sources. The number of diabetic patients roughly increased and may reach more than 600 million by twenty years. Transforming data into valuable and helpful information is an effort for researchers to improve the performance of ML techniques. This paper applies several types of sampling to predict 1000 samples with attributes and three diabetes class types (Random Forest tree, Hoeffding tree, LWL, NB updatable, and support vector Machine). This paper focused on most parameters that affected overall prediction accuracy. ML performances have been measured depending on the accuracy and mean absolute error for several cross-validation values before Feature reduction and after feature minimization by applying feature selection methods. It shows that Gender, Age, Blood Sugar Level (HbA1c), Triglycerides (TG), and Body Mass Index (BMI) are the most impact attributes applied. It is also shown that the Random Forest tree was the best method (97.7 and 98.6 %) with and without feature minimization, respectively, but it has a higher performance by omitting some unbalanced features from the diabetic dataset. Weight minimization has also been applied to techniques like SVM to obtain a better-searching plane and a robust model. In addition, this study specifies which parameters have weight minimization with the required analysis. Also, the feature selection method was applied to gain memory and reduce time.

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Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network

Cited by 14 publications

References 17 publications

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Supervised Contrastive Learning with Angular Margin for the Detection and Grading of Diabetic Retinopathy

Feature Minimization for Diabetic Disorders High Performances Prediction System-based on Random Forest Tree

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