Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Mar 15;11(6):863.
doi: 10.3390/healthcare11060863.

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

Ghadah Alwakid  1 , Walaa Gouda  2 , Mamoona Humayun  3
Affiliations

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

Ghadah Alwakid et al. Healthcare (Basel). .

Abstract

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

Keywords: APTOS; diabetic retinopathy; image enhancement; vision loss.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The five phases of diabetic retinopathy, listed by severity.
Figure 2
Figure 2
An illustration of the DR detecting system process.
Figure 3
Figure 3
Samples of the proposed image-enhancement techniques: original, unedited image; then rendition of this same image with CLAHE; finally final enhanced image after applying ESRGAN.
Figure 4
Figure 4
CLAHE architecture.
Figure 5
Figure 5
ESRGAN architecture.
Figure 6
Figure 6
Illustrations of the same image, augmented with enhancement.
Figure 7
Figure 7
Illustrations of the same image augmented without enhancement.
Figure 8
Figure 8
Number of training images after using augmentation techniques.
Figure 9
Figure 9
Best results for both scenarios.
Figure 10
Figure 10
Best confusion matrix of Inception-V3 with enhancement (with CLAHE + ESRGAN).
Figure 11
Figure 11
Best confusion matrix of Inception-V3 without enhancement (without CLAHE + ESRGAN).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Atwany M.Z., Sahyoun A.H., Yaqub M. Deep learning techniques for diabetic retinopathy classification: A survey. IEEE Access. 2022;10:28642–28655. doi: 10.1109/ACCESS.2022.3157632. - DOI
    1. Amin J., Sharif M., Yasmin M. A review on recent developments for detection of diabetic retinopathy. Scientifica. 2016;2016:6838976. doi: 10.1155/2016/6838976. - DOI - PMC - PubMed
    1. Kharroubi A.T., Darwish H.M. Diabetes mellitus: The epidemic of the century. World J. Diabetes. 2015;6:850. doi: 10.4239/wjd.v6.i6.850. - DOI - PMC - PubMed
    1. Alwakid G., Gouda W., Humayun M. Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN. Preprints. 2023:2023020218. doi: 10.20944/preprints202302.0218.v1. - DOI - PMC - PubMed
    1. Mamtora S., Wong Y., Bell D., Sandinha T. Bilateral birdshot retinochoroiditis and retinal astrocytoma. Case Rep. Ophthalmol. Med. 2017;2017:6586157. doi: 10.1155/2017/6586157. - DOI - PMC - PubMed

LinkOut - more resources