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

In light of the expanding population, an automated framework of diseasedetection can assist doctors in the diagnosis of ocular diseases, yieldsaccurate, stable, rapid outcomes, and improves the success rate of earlydetection. The work initially intended the enhancing the quality of fundusimages by employing an adaptive contrast enhancement algorithm (CLAHE) andGamma correction. In the preprocessing techniques, CLAHE elevates the localcontrast of the fundus image and gamma correction increases the intensity ofrelevant features. This study operates on a AMDNet23 system of deep learningthat combined the neural networks made up of convolutions (CNN) and short-termand long-term memory (LSTM) to automatically detect aged macular degeneration(AMD) disease from fundus ophthalmology. In this mechanism, CNN is utilized forextracting features and LSTM is utilized to detect the extracted features. Thedataset of this research is collected from multiple sources and afterwardapplied quality assessment techniques, 2000 experimental fundus imagesencompass four distinct classes equitably. The proposed hybrid deep AMDNet23model demonstrates to detection of AMD ocular disease and the experimentalresult achieved an accuracy 96.50%, specificity 99.32%, sensitivity 96.5%, andF1-score 96.49.0%. The system achieves state-of-the-art findings on fundusimagery datasets to diagnose AMD ocular disease and findings effectivelypotential of our method.

AMDNet23: A combined deep Contour-based Convolutional Neural Network and Long Short Term Memory system to diagnose Age-related Macular Degeneration