Leveraging Eager Learning for Retinal Abnormality Classification with Local Binary Pattern Features

This study investigates the integration of Local Binary Pattern (LBP) features with eager learning models for the classification of retinal abnormalities, focusing on diseases like Diabetic Retinopathy (DR) and Age-Related Macular Degeneration (AMD). Using the Retinal OCT Images (optical coherence tomography) dataset, we show that LBP is an effective technique for capturing the textural patterns associated with retinal conditions. Among various classifiers, the Random Forest model achieved the highest performance, with superior accuracy, recall, and F1 scores. This highlights the potential of LBP features combined with eager learning for robust and efficient classification of retinal diseases. The approach offers significant advantages for time-sensitive medical applications, supporting early predictions and real-time clinical workflows. Despite promising results, challenges such as addressing false positives and improving precision for overlapping conditions remain. Future work should explore deep learning models, hybrid feature extraction, and optimization strategies to enhance performance. Overall, this research demonstrates the potential for automated retinal disease detection systems, contributing to improved early diagnosis and better management of vision-threatening conditions.