Abstract
Eye disease recognition is a challenging task, which usually requires years of medical experience. In this work, we conducted research that can be a start for the most versatile solution. We tried to solve the problem of the classification of different eye diseases using neural networks. The first step of this work consists of gathering all publicly available eye disease datasets and preprocessing them to make the experiments as generalized as possible. This led to the creation of a dataset composed of over 30,000 images. The aim was to teach the model the actual symptoms of the diseases instead of adjusting the results to a given part of the dataset. Several deep convolutional neural networks were used as feature extractors and they were combined with the Synergic Deep Learning model. We conducted experiments on the data and were able to achieve promising results.
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The work was supported by the Foundation for Supporting the Development of Radiocommunication and Multimedia Techniques, which provided a scholarship to help with the research process.
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Haraburda, P., Dabała, Ł. (2022). Eye Diseases Classification Using Deep Learning. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_14
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