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<p>Diabetic retinopathy (DR), a serious eye condition in diabetic patients, requires early and precise detection for effective treatment. Late diagnosis and poor blood sugar control exacerbate this condition, highlighting the need for improved diagnostic methods. We developed a novel algorithm combining advanced image processing with machine learning techniques, utilizing classifiers such as SVM, decision tree, logistic regression, and kNN. A key feature of our approach is the incorporation of Voronoi Diagrams, which enhances the algorithm’s ability to analyze complex image patterns. The algorithm was tested on 800 eye (fundus) images. The decision tree-based classifier, a part of the algorithm, demonstrated high precision and reliability in predicting DR, achieving an AUC of 0.964. The integration of Voronoi Diagrams significantly improved accuracy and reliability across various classifiers. This study demonstrates that our algorithm, particularly the decision tree classifier, can diagnose DR with a level of accuracy comparable to established clinical benchmarks. The high AUC value confirms its effectiveness. Voronoi Diagrams notably enhanced the algorithm’s performance, indicating a promising approach for refining AI tools in ophthalmology.</p>

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Enhanced performance in automated diabetic retinopathy diagnosis achieved through Voronoi diagrams and artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月14日

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  • Mac Gayver da Silva Castro,
  • Francisco Vagnaldo Fechine Jamacaru,
  • Manoel Odorico de Moraes Filho,
  • Paulo Roberto Leitão de Vasconcelos,
  • Conceição Aparecida Dornelas

摘要

Diabetic retinopathy (DR), a serious eye condition in diabetic patients, requires early and precise detection for effective treatment. Late diagnosis and poor blood sugar control exacerbate this condition, highlighting the need for improved diagnostic methods. We developed a novel algorithm combining advanced image processing with machine learning techniques, utilizing classifiers such as SVM, decision tree, logistic regression, and kNN. A key feature of our approach is the incorporation of Voronoi Diagrams, which enhances the algorithm’s ability to analyze complex image patterns. The algorithm was tested on 800 eye (fundus) images. The decision tree-based classifier, a part of the algorithm, demonstrated high precision and reliability in predicting DR, achieving an AUC of 0.964. The integration of Voronoi Diagrams significantly improved accuracy and reliability across various classifiers. This study demonstrates that our algorithm, particularly the decision tree classifier, can diagnose DR with a level of accuracy comparable to established clinical benchmarks. The high AUC value confirms its effectiveness. Voronoi Diagrams notably enhanced the algorithm’s performance, indicating a promising approach for refining AI tools in ophthalmology.