Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

Alwakid, Ghadah; Gouda, Walaa; Humayun, Mamoona

doi:10.20944/preprints202302.0097.v1

Cited by 10 publications

(3 citation statements)

References 38 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An enhancement upon traditional AHE methods, contrast limited adaptive histogram equalization (CLAHE) was introduced by clipping histograms to constrain the contrast (16). Recently, several studies have shown the advantages of CLAHE on DL-related tasks, such as predicting five stages of diabetic retinopathy (17), segmenting temporomandibular joint articular disks from magnetic resonance images (18), and classification of COVID-19 and other pneumonia cases (19).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation

Wang,

Guo,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Deep-learning-based semantic segmentation algorithms, in combination with image preprocessing techniques, can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, CLAHE has demonstrated efficacy in enhancing the segmentations algorithms across various modalities. Method: This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. Our approach employs a U-Net architecture with ResNet34 (pre-trained on ImageNet) serving as the decoder. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle. Results: The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD=0.13), while that trained on CLAHE achieved a Dice score of 0.717 (SD=0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. Discussion: Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analysis such as disease classification and risk stratification.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation

Wang,

Guo,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Diabetic retinopathy (DR) is a devastating condition that can lead to blindness in advanced stages. 6 – 8 The majority of diabetes patients suffer from non-proliferative retinopathy (NPD). In the beginning stages of the disease, the central retina has edema and hard exudates, which are lipids that possibly leaked from abnormal blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-enhanced diabetic retinopathy image classification

et al. 2023

Self Cite

View full text Add to dashboard Cite

Objective Diabetic retinopathy (DR) can sometimes be treated and prevented from causing irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) model is employed to accurately identify all five stages of DR. Methods The suggested methodology presents two examples, one with and one without picture augmentation. A balanced dataset meeting the same criteria in both cases is then generated using augmentative methods. The DenseNet-121-rendered model on the Asia Pacific Tele-Ophthalmology Society (APTOS) and dataset for diabetic retinopathy (DDR) datasets performed exceptionally well when compared to other methods for identifying the five stages of DR. Results Our propose model achieved the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100% for the APTOS dataset, and the highest test accuracy of 79.67%, top-2 accuracy of 92.%76, and top-3 accuracy of 98.94% for the DDR dataset. Additional criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS and DDR. Conclusions It was discovered that feeding a model with higher-quality photographs increased its efficiency and ability for learning, as opposed to both state-of-the-art technology and the other, non-enhanced model.

show abstract

“… 3 , 4 Nevertheless, with prophylactic therapy and severe surveillance, roughly 56% of new DR cases can be reduced. 4 , 5 Microaneurysms, hard/soft exudates, and hemorrhages are examples of lesion-based symptoms that the ophthalmologist looks for in retinal pictures to determine the severity stages of DR. 6 , 7 There are several levels to a DR in a good direction 5 , 8 : There are four stages of atherosclerosis: (i) mild, in which microaneurysms can still be contained; (ii) moderate, wherein veins lose their ability to carry blood; (iii) severe, when blood channel blockages form and alert the body to produce new blood cells; and (iv) proliferative, wherein new arteries start sprouting. Some fundus pictures illustrating the different DR severity levels are depicted in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Enhancing diabetic retinopathy classification using deep learning

Alwakid,

Gouda,

Humayun

et al. 2023

DIGITAL HEALTH

Self Cite

View full text Add to dashboard Cite

Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the “Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection” dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL.

show abstract

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

Cited by 10 publications

References 38 publications

Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation

Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation

Deep learning-enhanced diabetic retinopathy image classification

Enhancing diabetic retinopathy classification using deep learning

Contact Info

Product

Resources

About