Diagnosing Glaucoma from Retinal Fundus Images with the Random Forest Technique

The prevalence of glaucoma as a major cause of irreversible blindness worldwide necessitates advancements in early detection and management methodologies. This paper introduces a novel automated approach to glaucoma detection using the Random Forest (RF) algorithm applied to retinal fundus images, known for its robustness in handling high-dimensional data and pattern recognition capabilities. Our methodology involves a comprehensive feature extraction process to identify key indicators of glaucomatous changes in the retina, enhancing early diagnostic accuracy. We detail the implementation of the RF algorithm, supported by mathematical models to optimize its diagnostic precision. Through rigorous validation on diverse datasets, our method demonstrated superior accuracy and efficiency compared to traditional and other machine learning-based approaches. The RF model showed promising results, achieving an accuracy of $\mathbf{9 2 \%}$, sensitivity of $\mathbf{8 9 \%}$, specificity of $95 \%$, and an area under the receiver operating characteristic curve of 0.97. These findings establish a significant step towards revolutionizing glaucoma diagnostics, offering a scalable and reliable tool for healthcare professionals. This study not only contributes to the field of network optimization but also underscores the potential of genetic algorithms in meeting the dynamic demands of wireless network setup, suggesting pathways toward more adaptive and intelligent network solutions.