Diabetic Retinopathy Grade Classification based on Fractal Analysis and Random Forest

Alzami, Farrikh; Abdussalam,; Megantara, Rama Arya; Fanani, Ahmad Zainul; Purwanto, Purwanto

doi:10.1109/isemantic.2019.8884217

Cited by 42 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Alzami et al [22] focused on stable subjects and patients with diabetic retinopathy but on extreme levels of patients with diabetic retinopathy and fractal components. By using MESSIDOR datasets and the classification of Random Forest, the author achieved the results where fractal measurements can discriminate between healthy patients and patients with diabetic retinopathy.…”

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection

Sapra,

Bhardwaj

et al. 2024

View full text Add to dashboard Cite

According to the International Diabetes Federation, there are 463 million diabetics worldwide. Due to alterations in lifestyle, the disease has had a significant negative influence on the quality of life for many people and is now seen as a global threat. Diabetic retinopathy (DR) is a leading cause of blindness among diabetic patients, emphasizing the need for early detection and intervention to prevent irreversible vision loss. It is caused by uncontrolled blood sugar and results in damage to blood vessels in the retina. Uncontrolled Type-1 diabetes and Type-2 diabetes both contribute to diabetic retinopathy. People with diabetes are more vulnerable to the severe effects of COVID-19. To prevent adverse consequences, early detection of the disease is essential. Therefore, automatic detection of diabetic retinopathy diseases is performed only by using computational techniques is a great solution. In this study, a deep learning model with an enhanced feature selection method has been constructed for the goal of early disease diagnosis. Also, the performance of the optimized dataset is evaluated using different machine learning methods such as Random Forest, FURIA, and decision tree. The proposed deep learning model achieved the highest accuracy of 93.5% with an optimized feature subset whereas random forest achieved a maximum accuracy of 92.26% with the optimized dataset and 91.2% and 89.4% with CFS-PSO and Information Gain.

show abstract

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection

Sapra,

Bhardwaj

et al. 2024

View full text Add to dashboard Cite

show abstract

“…CLAHE is suitable for improving the local contrast and enhancing the edge sharpness in each area of a fundus image (Figure 7B). As one of the obvious features of DR is the capillary leak syndrome when a fluid leaks from the small blood vessels into the surrounding tissues, many previous studies enhanced a part of the blood vessels [35][36][37] and ensured that the training model obtained an effective classification performance for different types of DR.…”

Section: Create a Crop Around The Image Center Of The Fundus Imagementioning

confidence: 99%

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

Lin

Jiang

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…F. Alzami et al [ 9 ] used fractal dimensions for retinopathy detection. Fractal dimensions are used to observe the vascular system of the retinal part of the eye, it not only detects the DR, but also the severity of the disease.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

et al. 2022

View full text Add to dashboard Cite

Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e., mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative Diabetic Retinopathy. The disease has no noticeable symptoms at early stages and may lead to chronic destruction, thus causing permanent blindness if not detected at an early stage. The proposed research provides deep learning frameworks for autonomous detection of Diabetic Retinopathy at an early stage using fundus images. The first framework consists of cascaded neural networks, spanned in three layers where each layer classifies data into two classes, one is the desired stage and the other output is passed to another classifier until the input image is classified as one of the stages. The second framework takes normalized, HSV and RGB fundus images as input to three Convolutional Neural Networks, and the resultant probabilistic vectors are averaged together to obtain the final output of the input image. Third framework used the Long Short Term Memory Module in CNN to emphasize the network in remembering information over a long time span. Proposed frameworks were tested and compared on the large-scale Kaggle fundus image dataset EYEPAC. The evaluations have shown that the second framework outperformed others and achieved an accuracy of 78.06% and 83.78% without and with augmentation, respectively.

show abstract

Diabetic Retinopathy Grade Classification based on Fractal Analysis and Random Forest

Cited by 42 publications

References 12 publications

Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection

Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

Contact Info

Product

Resources

About