Abstract
Glaucoma is one of the leading causes of irreversible blindness worldwide, and a correct and early diagnosis can impact on the disease treatment. The development of automated solutions for glaucoma detection, using digital image processing techniques and machine learning classifier models may enhance the conventional diagnosis methods. In this work, a new approach for feature extraction, based on projection vectors, was proposed as input to a Multilayer Perceptron (MLP) classifier for accomplishing automated glaucoma detection. Experimental results show that the proposed method provides good classification performance compared to state-of-the-art techniques: 91.6% of sensitivity, 94.5% of specificity, and 93.5% of accuracy. The proposed vectors may be useful in other artificial vision tasks.
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Urquijo, J.A.G., Sánchez Fonseca, J.D., López López, J.M., Cancino Suárez, S. (2021). Novel Features for Glaucoma Detection in Fundus Images. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_35
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DOI: https://doi.org/10.1007/978-3-030-77004-4_35
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