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Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices

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Abstract

Glaucoma is a disease that arises from increased intraocular pressure and leads to irreversible partial or total loss of vision. Due to the lack of symptoms, this disease often progresses to more advanced stages, not being detected in the early phase. The screening of glaucoma can be made through visualization of the retina, through retinal images captured by medical equipment or mobile devices with an attached lens to the camera. Deep learning can enhance and increase mass glaucoma screening. In this study, domain transfer learning technique is important to better weight initialization and for understanding features more related to the problem. For this, classic convolutional neural networks, such as ResNet50 will be compared with Vision Transformers, in high and low-resolution images. The high-resolution retinal image will be used to pre-trained the network and use that knowledge for detecting glaucoma in retinal images captured by mobile devices. The ResNet50 model reached the highest values of AUC in the high-resolution dataset, being the more consistent model in all the experiments. However, the Vision Transformer proved to be a promising technique, especially in low-resolution retinal images.

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Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.

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Authors and Affiliations

  1. Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801, Vila Real, Portugal

    Roberto Flavio Rezende, Ana Coelho, Rodrigo Fernandes, Alexandre Neto & António Cunha

  2. Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465, Porto, Portugal

    Alexandre Neto & António Cunha

  3. Departamento de Ciências e Tecnologia, Universidade Aberta, 1250-100, Lisbon, Portugal

    José Camara

Authors
  1. Roberto Flavio Rezende

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  2. Ana Coelho

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  3. Rodrigo Fernandes

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  4. José Camara

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  5. Alexandre Neto

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  6. António Cunha

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Corresponding author

Correspondence toAntónio Cunha.

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Editors and Affiliations

  1. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    António Cunha

  2. University of Beira Interior, Covilha, Portugal

    Nuno M. Garcia

  3. Ossietzky Universität Oldenburg, Oldenburg, Niedersachsen, Germany

    Jorge Marx Gómez

  4. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Sandra Pereira

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Rezende, R.F., Coelho, A., Fernandes, R., Camara, J., Neto, A., Cunha, A. (2023). Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices. In: Cunha, A., M. Garcia, N., Marx Gómez, J., Pereira, S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-32029-3_1

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