Citation :

Sumalatha Aradhya, Nehal Revuri, "Optimization of Ocular Cancer Anomaly Detection using Generative AI-based Multi-Scale Transformation Model," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 3, pp. 181-199, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I3P114

Abstract

Ocular Disease affects millions of people, and if not treated in advance, it may cause vision loss. The traditional way of detecting the disease is not accurate due to image filtering issues. In this paper, an optimal algorithm to improve diagnostic accuracy is proposed. The proposed framework facilitates ease of use, an intuitive interface to scan the eye images, to analyze the images, and to generate a comprehensive report of disease classifications and predictions using a generative AI-based multi-scale transformer model. The results obtained prove that the proposed system achieves the highest level of Accuracy in detecting and diagnosing eye diseases. The proposed solution uses the Visual Transformation Technique (VIT), and the proposed model eases the detection and diagnosis of eye diseases and cancer cells with an accuracy of 99.9%. The developed solution can be used by the ophthalmologist to ensure that patients receive prompt and accurate treatment, irrespective of the localities where there are fewer facilities, to accurately detect multiple anomalies.

Keywords

Gen AI, Transformer Models, ViT, VGG19, Ocular Cancer Cell, Anomalies Detection, Optical Coherence Tomography (OCT).

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