mlsanityData to 14 Oct 2026

Retinal Malady Classification using AI: A novel ViT-SVM combination architecture

arXiv 2603.29181 ↗First posted 31 Mar 2026eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 31 Mar 2026.

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References · 3

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Going deeper with Image Transformerscited by 764
  3. 2018A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseasescited by 16

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