# Diabetic Retinopathy Improved Detection Using Deep Learning
## 5 Conclusions  
In this work, we presented a model to detect diabetic retinopathy at early stages as an auxiliary diagnostic tool. The Messidor and APTOS dataset helped to check the model's feature acquisition for different stages of DR directly from examination images. Our proposal achieved a good performance under both unbalanced datasets with 81% and 64% for APTOS and Messidor datasets, respectively. A fair comparison of this work with previous methods is still missing given the lack of a standardized test dataset and methodology to propose a new solution to classify DR subclasses. A standardized dataset becomes necessary given two main reasons. The first one is related to the datasets sources and the institution which manages and ensures label integrity. The second one is related to the data used to get the validation metrics because each work presents a different validation data split; hence, the tested data between one model and another is not the same. Additionally, many data competitions have no public test labels, making it difficult to compute the test metrics. Nevertheless, we demonstrated that the APTOS dataset presents a more variate distribution of images to optimize the model acquiring better features to classify DR subclasses. In future works, we propose to create a benchmark dataset to follow as a baseline comparison. Additionally, check different preprocessing functions and architecture, such as a multilabel approach to improve the model performance. The multilabel method comes from the idea that one kind of NPDR shares features between them. Moreover, a detection algorithm with a bounding box also will be studied. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**DS** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{AM} & Healthy & 0.60 & **1.00** & **0.75** \\  & Unhealthy & **0.96** & 0.07 & 0.14 \\ \hline \multirow{2}{*}{MA} & Healthy & **0.69** & 0.39 & 0.50 \\  & Unhealthy & 0.58 & **0.83** & **0.68** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics obtained during testing as binary classification. DS columns indicate the datasets used for training and testing. In that order, A means APTOS, and M means Messidor. Bolder values indicate best performance for a specific class between both datasets. Class column Healthy comprises only No DR class, while Unhealthy comprises remaining ones to achieve a suitable comparison between our proposal and previous works. **Author Contributions:** Conceptualization, T.O.F. and F.C. ; funding acquisition, F.C. and B.F.; investigation, T.O.F. and A.A.; methodology, T.O.F. and A.A.; supervision, F.C. ; writing--original draft, A.A. and T.O.F. ; writing--review & editing, A.A., B.F. and F.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001, Fundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco (FACEPE), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)--Brazilian research agencies, and Universidad Central de Chile under the research project CIP2020013. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. * _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. _JAMA_**2016**, _316_, 2402-2410. [CrossRef] [PubMed]
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