English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Medical imaging plays a significant role in detecting and treating variousdiseases. However, these images often happen to be of too poor quality, leadingto decreased efficiency, extra expenses, and even incorrect diagnoses.Therefore, we propose a retinal image enhancement method using a visiontransformer and convolutional neural network. It builds a cycle-consistentgenerative adversarial network that relies on unpaired datasets. It consists oftwo generators that translate images from one domain to another (e.g., low- tohigh-quality and vice versa), playing an adversarial game with twodiscriminators. Generators produce indistinguishable images for discriminatorsthat predict the original images from generated ones. Generators are acombination of vision transformer (ViT) encoder and convolutional neuralnetwork (CNN) decoder. Discriminators include traditional CNN encoders. Theresulting improved images have been tested quantitatively using such evaluationmetrics as peak signal-to-noise ratio (PSNR), structural similarity indexmeasure (SSIM), and qualitatively, i.e., vessel segmentation. The proposedmethod successfully reduces the adverse effects of blurring, noise,illumination disturbances, and color distortions while significantly preservingstructural and color information. Experimental results show the superiority ofthe proposed method. Our testing PSNR is 31.138 dB for the first and 27.798 dBfor the second dataset. Testing SSIM is 0.919 and 0.904, respectively.

Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network