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Diabetic retinopathy (DR) is one of the many consequences caused by diabetes mellitus (DM). The way to detect DR is through different distinctive features that can be observed through retinal images. With this goal, expert technicians look for the characteristic features in the images for the correct detection of the disease. However, some authors dedicated to research have used convolutional neural networks (CNNs). Due to the good results offered by this technique, this paper presents the implementation of Type-1 fuzzy logic to combine it with convolutional neural networks to increase performance of the obtained results. The implementation of fuzzy logic to adjust the hyperparameters allowed us to obtain a mean precision of 0.9273 with a standard deviation of 0.0130, offering better results than when Type-1 fuzzy logic is not implemented, where a mean precision of 0.9021 was obtained with a standard deviation of 0.1065.

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Estimation of Filter Number for Convolutional Neural Networks with Fuzzy Logic for Diabetic Retinopathy Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Rodrigo Cordero-Martínez,
  • Daniela Sánchez,
  • Oscar Castillo,
  • Patricia Melin

摘要

Diabetic retinopathy (DR) is one of the many consequences caused by diabetes mellitus (DM). The way to detect DR is through different distinctive features that can be observed through retinal images. With this goal, expert technicians look for the characteristic features in the images for the correct detection of the disease. However, some authors dedicated to research have used convolutional neural networks (CNNs). Due to the good results offered by this technique, this paper presents the implementation of Type-1 fuzzy logic to combine it with convolutional neural networks to increase performance of the obtained results. The implementation of fuzzy logic to adjust the hyperparameters allowed us to obtain a mean precision of 0.9273 with a standard deviation of 0.0130, offering better results than when Type-1 fuzzy logic is not implemented, where a mean precision of 0.9021 was obtained with a standard deviation of 0.1065.