Detection of retinal diseases using an accelerated reused convolutional network
This work addresses the need for more accessible deep neural network models for eye disease detection, particularly for use on mobile devices, though it appears incremental as it builds upon existing convolutional network designs.
The paper tackled the problem of computationally complex methods for detecting retinal diseases by redesigning convolutional layers to create a new general model with ArConv layers, resulting in a model with only 1.3 million parameters that achieved an accuracy of 0.9328 on the RfMiD test set, outperforming MobileNetV2's 0.9266.
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.