Classification of Retinoblastoma Eye Disease on Digital Fundus Images Using Geometric Features and Machine Learning

Medical image analysis is essential for detecting retinoblastoma tumors due to the ability of this method to assist doctors in examining the morphology, density, and distribution of blood vessels. The classification of normal and retinoblastoma-affected retinas is a preliminary step in treating retinoblastoma tumors. Therefore, this study aimed to propose a new method for classifying normal and retinoblastoma-affected retinas using geometric feature extraction and machine learning. The workflow consisted of (1) fundus image data collection for retinoblastomas, (2) image segmentation, (3) feature extraction process, (4) building a classification model using machine learning, (5) splitting testing and training data, (6) classification process using machine learning methods, and (7) evaluation of classification results using a confusion matrix. The results showed that the segmentation method could detect retinoblastoma areas and extract their geometric features. The SVM method achieved an accuracy of 0.96 while the RF and DT had 0.55 and 0.63, respectively. Moreover, a comparison with previous research showed that the proposed method achieved a 4% improvement in the classification performance. This led to the conclusion that classification using geometric features combined with the SVM on digital fundus images of retinoblastoma eye disease produced the best results.