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

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

retinoblastoma; digital fundus images; classification; geometric features; machine learning

Medical image analysis is essential for detecting retinoblastoma tumors due to the ability of the 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 stepin treating retinoblastoma tumors. Therefore, this research aimed to propose the new development of a method to classify 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 used could detect retinoblastoma areas and extract geometric features. The SVM method achieved an accuracy of 0.96 while the RF andDT had 0.55 and 0.63, respectively. Moreover, the comparison with previous research showed that the method proposed had a 4% improvement in classification performance. This led to the conclusion that the classification using geometric features combined with the SVM on digital fundus images of retinoblastoma eye disease produced the best results

Arif Setiawan

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6337/1058

Department of Information System, Faculty of Engineering, Muria Kudus University, Kudus, Indonesia

May 24, 2025

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