Retinal Malady Classification using AI: A novel ViT-SVM combination architecture
This addresses early detection of retinal diseases like macular holes and diabetic retinopathy for medical diagnosis, but appears incremental as it combines existing methods.
The study tackled automated classification of retinal diseases from OCT scans using a hybrid Vision Transformer and Support Vector Machine (ViT-SVM) architecture, but no concrete performance numbers were provided in the abstract.
Macular Holes, Central serous retinopathy and Diabetic Retinopathy are one of the most widespread maladies of the eyes responsible for either partial or complete vision loss, thus making it clear that early detection of the mentioned defects is detrimental for the well-being of the patient. This study intends to introduce the application of Vision Transformer and Support Vector Machine based hybrid architecture (ViT-SVM) and analyse its performance to classify the optical coherence topography (OCT) Scans with the intention to automate the early detection of these retinal defects.