Predicting Glaucoma Severity Levels in Human Eyes: Integrating CNN and Random Forest for accurate classification

This study prompts intervention and effective treatment, it is crucial to accurately categorize the various levels of glaucoma severity in human eyes. To accurately classify glaucoma sickness, this research introduces a novel methodology that combines Convolutional Neural Networks, also known as CNNs, and Random Forests synergistically. Our model displays amazing accuracy in differentiating between intensity classes by taking advantage of CNNs' skill at extracting complex information from medical pictures and Random Forests' talent for ensemble learning. The method includes careful data augmentation, preprocessing, and aggregation, which strengthens the model's durability. The effectiveness of the model over a range of glaucoma levels of severity may be understood by a thorough performance evaluation that takes variables like accuracy, recollection, and F1 score into account. The results highlight its potential as a tool for diagnosis for healthcare practitioners, enabling early detection. This study provides a transformational approach to improving glaucoma diagnosis and demonstrates the possibility of multidisciplinary collaboration between machine learning and medical field expertise. The results highlight the wider significance of technology-driven medical improvements in tackling complicated medical issues, with potential applications to other eye illnesses and beyond.