## 4 Conclusions  
We presented the training and testing of the Caffe and Keras model using retina images of healthy and at various stages of diabetic retinopathy. A total of 35,000 high-resolution images labeled by experts were used for training of the models. Another 53,000 images also labeled by experts were used for testing of the trained models. The classification results were aggregated as healthy and unhealthy. The unhealthy images included mild to proliferate diabetic retinopathy. The Caffe model  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Test Images**} & \multicolumn{2}{c}{**Classification**} \\ \hline
**Type** & **No. of Images** & **Healthy** & **Unhealthy** \\ \hline No DR & 39,533 & 98\% & 2\% \\ Mild DR & 3780 & 97\% & 3\% \\ Moderate DR & 7861 & 90\% & 10\% \\ Severe DR & 1214 & 70\% & 30\% \\ Proliferate DR & 1206 & 60\% & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results from the Caffe model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Test Images**} & \multicolumn{2}{c}{**Classification**} \\ \hline
**Type** & **No. of Images** & **Healthy** & **Unhealthy** \\ \hline No DR & 39,533 & 97\% & 3\% \\ Mild DR & 3780 & 97\% & 3\% \\ Moderate DR & 7861 & 80\% & 20\% \\ Severe DR & 1214 & 59\% & 41\% \\ Proliferate DR & 1206 & 45\% & 55\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results from the Keras model.  
classified healthy and unhealthy images with 75.6% accuracy and 98% specificity. The sensitivities for severe and proliferate diabetic retinopathy images were 30% and 40%, respectively. The Keras model classified healthy and unhealthy images with 76.7% accuracy and 97% specificity. However, the sensitivities for severe and proliferate diabetic retinopathy images significantly improved to 41% and 55%, respectively. The future goal of this research is to develop low-cost hardware capable of on-site real-time classification of retina images. As a result of the nature of the intended application, the specificity (true negative) needs to be close to 100% to reduce the risks associated with a wrong classification result. In addition, to reduce unnecessary tests and associated costs, the sensitivity of the system has to be further improved for a practical application. This can be achieved by further optimizing the presented model or considering alternative models such as recurrent neural network. The Caffe model was trained and tested on an NVIDIA embedded hardware. The Keras model has shown more promise and will be implemented on the same hardware, and the accuracy will be further improved. Although the accuracy needs to be further improved, the presented results represent a significant step forward in the direction of detecting diabetic retinopathy using embedded computer vision for implementation in portable low-cost hardware. This technology has the potential of being able to detect diabetic retinopathy without having to see an eye specialist in remote and medically underserved locations, which can have significant implications in reducing diabetes-related vision losses in the future.  
Both [PERSON] and [PERSON] contributed to writing and editing the paper, research design and results analysis, experimental work was done by [PERSON] under the supervision of [PERSON], and [PERSON] was responsible for project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the support and resources provided by the Intelligent Systems Lab and the Department of Engineering Science at Sonoma State University.  
The authors declare no conflict of interest.