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Neural Networks and Saliency Maps in Diabetic Retinopathy Diagnosis

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Computer Information Systems and Industrial Management (CISIM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14164))

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Abstract

Diabetes is one of the most dangerous illnesses. Sometimes it can be observed faster by experienced ophthalmologist rather than general practitioner. It is related to the fact that it leads to the small changes in the form of exudates visible in retina color images. Such amendments are known under name of “diabetic retinopathy”. However, it needs to be claimed that, in most of the cases, when detected it is too advanced to preserve patient’s eyesight. This is the main reason why it is urgent to work-out novel ideas and approaches that can detect these changes in their early stages. In this work we propose a neural network-based algorithm for diabetic retinopathy recognition. For the aim of classification, we consumed well-known CNN’s architectures as ResNet50 or VGG-16 but also, new ones as InceptionV4. The best results reached 95% in the task of diabetic retinopathy recognition. Moreover, to increase confidence, saliency maps were introduced – by this solution we observed which parts of the image had the highest impact on the classifier decisions. Our observations confirmed that exudates were the most important in the classification process.

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Acknowledgment

This work was supported by grant W/WI/4/2022 from Białystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.

The Author is grateful to experienced ophthalmologists who checked the results and provided their opinions about the processing pipeline and the marked regions within saliency maps.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Maciej Szymkowski

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  1. Maciej Szymkowski
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Correspondence to Maciej Szymkowski .

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Editors and Affiliations

  1. Bialystok University of Technology, Białystok, Poland

    Khalid Saeed

  2. VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Jiří Dvorský

  3. Tokyo Metropolitan University, Tokyo, Japan

    Nobuyuki Nishiuchi

  4. Fukuoka Institute of Technology, Fukuoka, Japan

    Makoto Fukumoto

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Szymkowski, M. (2023). Neural Networks and Saliency Maps in Diabetic Retinopathy Diagnosis. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-42823-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42822-7

  • Online ISBN: 978-3-031-42823-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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