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 is one of the growing fields in the world of computer vision.In this study, we aim to address the Diabetic Retinopathy (DR) problem as oneof the open challenges in medical imaging. In this research, we propose a newlesion detection architecture, comprising of two sub-modules, which is anoptimal solution to detect and find not only the type of lesions caused by DR,their corresponding bounding boxes, and their masks; but also the severitylevel of the overall case. Aside from traditional accuracy, we also use twopopular evaluation criteria to evaluate the outputs of our models, which areintersection over union (IOU) and mean average precision (mAP). We hypothesizethat this new solution enables specialists to detect lesions with highconfidence and estimate the severity of the damage with high accuracy.

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM