  Over the past few decades, convolutional neural networks (CNNs) have been at
the forefront of the detection and tracking of various retinal diseases (RD).
Despite their success, the emergence of vision transformers (ViT) in the 2020s
has shifted the trajectory of RD model development. The leading-edge
performance of ViT-based models in RD can be largely credited to their
scalability-their ability to improve as more parameters are added. As a result,
ViT-based models tend to outshine traditional CNNs in RD applications, albeit
at the cost of increased data and computational demands. ViTs also differ from
CNNs in their approach to processing images, working with patches rather than
local regions, which can complicate the precise localization of small, variably
presented lesions in RD. In our study, we revisited and updated the
architecture of a CNN model, specifically MobileNet, to enhance its utility in
RD diagnostics. We found that an optimized MobileNet, through selective
modifications, can surpass ViT-based models in various RD benchmarks, including
diabetic retinopathy grading, detection of multiple fundus diseases, and
classification of diabetic macular edema. The code is available at
https://github.com/Retinal-Research/NN-MOBILENET
