Authors - Rimon Kumer Roy, Jannatul Ferdous, Kazi Lutfur Nahar Mithila, Sabbir Islam, Mohammad Zahid Hassan, Sadah Anjum Shanto Abstract - Early identification of ophthalmic disease is critical to pre serve eyesight. We present DeepEye, a stacking-ensemble framework for multi-disease classification on the Eye Disease Image Dataset (EDID, Mendeley Data). After standardized preprocessing and augmentation, f ive architectures ResNet50, VGG16, DenseNet121, EfficientNet-B4, and Vision Transformer were trained and evaluated. The final ensemble in tegrates the top base models with a logistic regression meta-learner op timized via hyperparameter tuning. On a held-out test set, DeepEye achieves 91.34% accuracy and AUC of 0.9965, outperforming all con stituent models and exhibiting stable gains across cross validation folds. Model transparency is supported with Grad-CAM visualizations that lo calize disease-relevant regions, enhancing clinical interpretability. These results indicate that combining convolutional and transformer backbones within a tuned stacking framework yields a high-accuracy, explainable approach for automated eye disease detection in healthcare settings.
Open Zoom