Classification of Pseudopapilledema and Papilledema Using Decision Tree and Hu Moments

Pseudopapilledema, characterized by an anomalous elevation of the optic disc without retinal nerve fiber layer edema, often mimics the presentation of true papilledema caused by increased intracranial pressure. Accurate differentiation between these conditions is critical to avoid unnecessary invasive procedures. This study employs a Decision Tree classifier to classify optic disc images into three categories: normal, papilledema, and pseudopapilledema. The dataset, obtained from Kaggle, consists of imbalanced images segmented using the Canny edge detection method and features extracted using Hu Moments. The dataset was divided into 80% training and 20% testing sets. Performance was evaluated using 5-fold cross-validation, yielding an average accuracy of 53.61%, precision of 55.20%, recall of 54.12%, and F1-score of 55.17%. The study provides a comprehensive analysis of the classifier's performance, including visualizations such as segmentation results, scatter plots of Hu Moments, and confusion matrices. The results indicate that while the Decision Tree classifier demonstrates moderate effectiveness, there is significant room for improvement. The research highlights the potential of machine learning models in medical diagnostics but also underscores the need for more robust algorithms and diverse datasets. Future work should focus on incorporating more complex models and expanding the dataset to enhance diagnostic accuracy. These findings contribute to the field of medical image analysis and propose a non-invasive diagnostic tool that, when integrated with clinical expertise, could improve patient outcomes and reduce unnecessary procedures