Advanced International Journal for Research

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Volume 7 Issue 1
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Ensemble Machine Learning Classification of Eye Herpes (NAGIN) Using Shape Features on Simulated Ophthalmic Images

Author(s) Mr. Kakasaheb Rangnath Nikam, Prof. Dr. Ramesh Raybhan Manza
Country India
Abstract Accurate classification of ophthalmic lesions depends on robust feature engineering and transparent machine learning (ML) pipelines. We propose an ensemble ML‑based framework for diagnosing eye herpes (NAGIN) using a simulated dataset of 712 anterior eye images (350 herpes‑like, 362 non‑herpes). Five features - fractal dimension, solidity, eccentricity, branching index, and terminal bulb ratio, were extracted from segmented contours and used for supervised learning. Six ML classifiers were evaluated: logistic regression, support vector machine (RBF kernel), random forest, gradient boosting, XGBoost, and k‑nearest neighbours. Performance was assessed using accuracy, precision, recall, F1‑score, and AUC. Ensemble methods delivered perfect diagnostic outcomes, with Random Forest, Gradient Boosting, and XGBoost achieving 100% accuracy, F1‑score, and AUC. Logistic regression and SVM gave excellent results, with accuracy around 98.6% and an AUC close to 1. In comparison, KNN did a bit worse, reaching about 96.5% accuracy and an AUC of 0.983. Feature importance analysis identified fractal dimension, branching index, and terminal bulb ratio as dominant predictors, while correlation analysis reinforced biological plausibility by linking dendritic branching with fractal complexity and elongated morphology with reduced solidity. These results highlight the value of engineered shape features combined with ensemble ML strategies for reliable ophthalmic diagnosis and provide a scalable foundation for Explainable Artificial Intelligence (EAI) in Medical Image Analysis (MIA).
Keywords Explainable AI, Eye Herpes, ML Models, NAGIN, Shape-Based Descriptors
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2026
Published On 2026-01-22
DOI https://doi.org/10.63363/aijfr.2026.v07i01.3060
Short DOI https://doi.org/hbk6x2
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