Detection of retinal diseases using an accelerated reused convolutional network

• URL: http://arxiv.org/abs/2510.04232v1
• Date: Sun, 05 Oct 2025 14:44:09 GMT
• Title: Detection of retinal diseases using an accelerated reused convolutional network
• Authors: Amin Ahmadi Kasani, Hedieh Sajedi,
• Abstract summary: We create a new general model that incorporates our novel convolutional layer named ArConv layers.<n>The model has suitable complexity for use in mobile phones and can perform the task of diagnosing the presence of disease with high accuracy.
• Score: 3.5808917363708743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Convolutional neural networks are continually evolving, with some efforts aimed at improving accuracy, others at increasing speed, and some at enhancing accessibility. Improving accessibility broadens the application of neural networks across a wider range of tasks, including the detection of eye diseases. Early diagnosis of eye diseases and consulting an ophthalmologist can prevent many vision disorders. Given the importance of this issue, various datasets have been collected from the cornea to facilitate the process of making neural network models. However, most of the methods introduced in the past are computationally complex. In this study, we tried to increase the accessibility of deep neural network models. We did this at the most fundamental level, specifically by redesigning and optimizing the convolutional layers. By doing so, we created a new general model that incorporates our novel convolutional layer named ArConv layers. Thanks to the efficient performance of this new layer, the model has suitable complexity for use in mobile phones and can perform the task of diagnosing the presence of disease with high accuracy. The final model we present contains only 1.3 million parameters. In comparison to the MobileNetV2 model, which has 2.2 million parameters, our model demonstrated better accuracy when trained and evaluated on the RfMiD dataset under identical conditions, achieving an accuracy of 0.9328 versus 0.9266 on the RfMiD test set.

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