Loganathan R

Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people’s lives and severely impairs their visual acuity. AMD is becoming more common, requiring improved diagnostic and prognostic methods. Treatment efficacy and patient survival rates stand to benefit greatly from these upgrades. To improve AMD diagnosis in preprocessed retinal images, this study uses Grey Level Co-occurrence Matrix (GLCM) features for texture analysis. The selected GLCM features include contrast and dissimilarity. Notably, grayscale pixel values were also integrated into the analysis. Key factors such as contrast, correlation, energy, and homogeneity were identified as the primary focuses of the study. Various supervised machine learning (ML) and CNN techniques were employed on Optical Coherence Tomography (OCT) image datasets. The impact of feature selection on model performance is evaluated by comparing all GLCM features, selected GLCM features, and grayscale pixel features. Models using GSF features showed low accuracy, with OCTID at 23% and Kermany at 54% for BC, and 23% and 53% for CNN. In contrast, GLCM features achieved 98% for OCTID and 73% for Kermany in RF, and 83% and 77% in CNN. SFGLCM features performed the best, achieving 98% for OCTID across both RF and CNN, and 77% for Kermany. Overall, SFGLCM and GLCM features outperformed GSF, improving accuracy, generalization, and reducing overfitting for AMD detection. The Python-based research demonstrates ML’s potential in ophthalmology to enhance patient outcomes.

This paper proposes new computational strategies to improve optical coherence tomography image quality for age-related macular degeneration identification. Integrated into an age-related macular degeneration detection system, these algorithms automate and improve the identification of abnormalities in optical coherence tomography images, aiding in the classification of normal and abnormal macular tissues. The research presents an innovative approach to detecting age-related macular degeneration related anomalies, combining texture analysis, statistical evaluation, and genetic algorithms for feature selection. Genetic algorithm optimization finds the best predictive characteristics by using in-depth texture analysis with the gray level co-occurrence matrix and comprehensive statistical research. Gray level co-occurrence matrix features are analyzed at four angles (0°, 45°, 90°, and 135°), with the random forest classifier trained using optimized features. The random forest classifier plays a vital role in both the training and testing phases, achieving no-table results: an error rate of 0% for selected features, 1.9% for all features, and 7.5% for no features, and an overall system classification accuracy of 100% for training data for all, while maintaining 92.458% 98.113%, 100% for testing data of no features, all features and selected features.